Learning Bayesian Statistics

Today's clip is from Episode 164, featuring Andrew Gelman, Aki Vehtari & Richard McElreath. In this conversation, Andrew explains how Bayesian principal stratification can be used to reason about treatment effects when there is an intermediate treatment or outcome that is only partially observed.

He discusses how latent variables can represent whether someone would take a stage-two treatment, and how pre-treatment characteristics such as age, location, and past spending can help build a model for this process.

Richard connects the problem to the broader distinction between per-protocol and intent-to-treat analyses, and they discuss how standard approaches such as instrumental variables can be understood as special cases of more general Bayesian models. It's a useful example of how Bayesian modeling can represent the full process behind a causal question rather than relying on simplifying assumptions.

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Today's clip is from Episode 164, featuring Andrew Gelman, Aki Vehtari & Richard McElreath. In this conversation, Andrew explains why a Bayesian workflow goes far beyond simply fitting a model.

He discusses the importance of building, fitting, and checking models, and why moving between simpler and more complicated models can reveal insights that a single model might miss.

He also explores how simulation and generative modeling can help researchers evaluate new models and gain confidence in their results, even when there isn't an established method or published study to rely on. It's a look at why good statistical practice isn't just about getting an answer, but knowing how much you can trust it.

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Support & Resources
→ Support the show on Patreon
→ Bayesian Modeling Course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work

Takeaways:
Q: What is the "Bayesian Workflow" book about, and who is it for?

A: It covers what the three authors know that isn't already in Bayesian Data Analysis (BDA3) or Statistical Rethinking, organized around case studies that walk through full analyses end to end rather than just giving a recommendation. It's not an introduction to Bayesian inference -- it assumes you already know the basics -- but a guide to making theoretically informed, professional decisions at the many branching points a real analysis involves that source books rarely acknowledge.

Q: What's a concrete way to report Bayesian results without just handing over a posterior distribution?

A: Report a few named scenarios from the distribution, such as pessimistic, median, and optimistic. This is easier to discuss than a full posterior and helps shift the conversation toward what would move outcomes from the median toward the optimistic case.

Full takeaways

Chapters:
00:18:22 What is the elevator pitch for the Bayesian Workflow book?
00:20:12 Where does workflow sit between statistical theory and case studies?
00:27:21 Why express your scientific background in a generative model?
00:36:43 How is a Bayesian workflow different from a pipeline?
00:39:03 What is reverse Bayes, and how does it help with prior sensitivity?
00:43:53 How do Bayesians reinterpret non-Bayesian methods?
00:45:02 How is the Bayesian Workflow book structured?
00:48:49 How do you model bat mortality at wind farms from zero-inflated carcass counts?
00:52:24 When does a hierarchical model stop being an innocuous assumption?
00:58:17 Can multilevel regression and poststratification pool detection across sites?
00:59:32 Why start with a big generative simulation before the statistical model?
01:02:05 What is the "secret weapon" of comparing shrinkage to fixed-effects estimates?
01:11:02 How do you detect which assumptions are actually driving your inference?
01:15:24 How do you get regulated industries to accept a posterior instead of a score?
01:22:04 Should statisticians soften uncertainty for decision makers?
01:23:11 Why report three scenarios instead of a single number?
01:27:51 How do you handle a leaky instrument in causal inference?
01:29:16 What is a principal stratification model?
01:34:47 What are the three authors working on next?

Thank you to my Patronsfor making this episode possible!

Full show notes

Today's clip is from Episode 154, featuring Thomas Pinder. In this conversation, Thomas shares what he sees as the next steps for GPJax and how the project could become easier to use beyond its original research-focused audience.

He discusses creating a higher-level interface that could make fitting Gaussian processes possible in just a few lines of code, while still keeping the flexibility and infrastructure that GPJax provides. He also talks about making the documentation more engaging by moving beyond synthetic examples and showcasing real-world applications, such as modeling ocean currents with Gaussian processes.

It's a look at how GPJax could evolve from a powerful research tool into something that's even more accessible and practical for a wider range of users.

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Today's clip is from Episode 163, featuring Eliot Carlson and Adrian Seyboldt. In this conversation, Eliot and Adrian look beyond current approaches to HMC adaptation and preconditioning and share the ideas they're most excited to explore next.

Eliot discusses new ways of parallelizing MCMC by solving for an entire trajectory at once rather than computing every step sequentially, a potentially powerful direction for expensive, high-dimensional problems. Adrian, meanwhile, talks about exploring non-adjusting methods and going beyond first-order information by investigating how higher-order autodiff and second-order derivatives could open up new possibilities for sampling.

It's a glimpse into some of the ideas that could help make MCMC faster and more scalable as computational hardware continues to become increasingly parallel.

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Support & Resources
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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work

Takeaways:

Q: What is mass matrix adaptation, in plain terms?
A: Mass matrix adaptation is best understood as an automatic, fairly dumb, but very effective reparameterization of your model. The simplest version, the diagonal mass matrix, just rescales each parameter so its posterior standard deviation becomes one, which is exactly what you'd do by hand if you had the patience. Every time you sample a PyMC or Stan model, this kind of reparameterization is happening under the hood.

Q: How does Nutpie's approach to mass matrix adaptation differ from Stan and PyMC's default?
A: Stan and PyMC's default sampler only use one source of information for diagonal mass matrix adaptation: the posterior standard deviation estimated from warm-up draws. Nutpie also uses the gradients of the log density, which HMC is already computing at every step to build its trajectory. For a standard normal distribution, the covariance of the gradients is exactly the inverse covariance of the draws, so Nutpie takes the geometric mean of the two resulting standard deviations. There's no guarantee it's always better, but in practice it usually is.

Q: What problem does "Preconditioning Hamiltonian Monte Carlo by Minimizing Fisher Divergence" actually solve?
A: Preconditioning HMC means transforming your target distribution into one that's friendly to sample, but doing that well requires knowing things about the distribution, like its covariance, that sampling itself is supposed to discover. This chicken-and-egg problem is usually handled by sketching a rough estimate from a handful of early warm-up draws, which can burn a large share of total sampling time. Adrian and Eliot's paper formalizes how to make better use of a second signal, the score function, that HMC already computes for free but that Stan-style preconditioning ignores.


Chapters:
00:00:00 What is HMC preconditioning?
00:09:03 A more robust low-rank mass matrix
00:11:58 What is mass matrix adaptation?
00:18:06 What does preconditioning HMC mean?
00:20:57 What is normalizing flow adaptation, and when does a linear mass matrix fall short?
00:23:50 When does normalizing flow adaptation actually help, and when is classic mass matrix adaptation enough?
00:27:13 What is Fisher divergence?
00:30:10 Why is HMC's trajectory, not its density, the right target for preconditioning?
00:33:04 What are the diagonal, dense, and low-rank-plus-diagonal versions of mass matrix adaptation?
00:46:25 How much faster is low-rank-plus-diagonal adaptation?
00:51:07 What's the practical recommendation for using Nutpie and its mass matrix adaptation?
00:54:31 Why does low-rank adaptation sometimes fail spectacularly?
01:01:35 Where does this research fit in the bigger picture of HMC?
01:12:12 How could centered vs. non-centered parameterization be chosen automatically?

Thank you to my Patronsfor making this episode possible!

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Today's clip is from episode 160, featuring Vaden Masrani. In this conversation, Vaden explores the tension between Bayesian statistics and Bayesian epistemology, and why he sees them as fundamentally different.

He explains why Bayesian epistemology can run into problems when trying to explain where hypotheses themselves come from, and argues that an emphasis on finding supporting evidence can encourage confirmation bias rather than genuine scientific inquiry. He also discusses Hempel's paradox, Popper's idea of falsification, and why these philosophical problems don't necessarily undermine Bayesian statistics itself.

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Today's clip is from episode 160, featuring Vaden Masrani. In this conversation, Vaden lays out a sharp critique of Bayesian epistemology - the roughly hundred-year-old philosophical tradition, popular in some Oxford-adjacent circles, that treats subjective probability estimates as legitimate even when there's no data behind them.

Vaden's core objection: doing Bayes' theorem on numbers you made up in your head is like fitting a regression line to an empty scatter plot - the math looks rigorous, but there's nothing underneath it. He argues this "math-washing" can trick people into thinking a decision is well-informed simply because it's dressed up in probability language, when frequentists and data-driven Bayesians alike would say the same thing: no data, no model.

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Today's clip is from episode 162, featuring Chris Krapu. In this conversation, Chris explains why Bayesian thinking remains surprisingly valuable in today's AI landscape - even when the models themselves aren't explicitly Bayesian.

Rather than uncertainty estimation, Chris highlights a different advantage: Bayesian training provides a deep intuition for concepts like priors, sampling, rejection sampling, and high-dimensional geometry, making it much easier to understand and apply modern AI research. He also discusses why Bayesian methods are becoming increasingly relevant for evaluating agentic AI systems, where complex workflows and limited evaluation data make hierarchical models and sensible priors especially powerful.

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Support & Resources
→ Support the show on Patreon
→ Bayesian Modeling Course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work

Takeaways:

Q: How does putting a Gaussian process on unknown coordinates fix noisy location data in mineral prospecting?

A: In mining and geostatistics, the classic Gaussian process model, known there as kriging, assumes you know exactly where each sample was taken. Chris’ project broke that assumption on purpose: the recorded coordinates for each core sample were only accurate to within a rough radius. By treating the true locations as latent variables and putting a Gaussian process over them jointly with the measurements, the model could still reconstruct the underlying gold-concentration field, even though the exact sampling locations were never known precisely. It's a demonstration that Gaussian processes can absorb structural uncertainty that looks, at first glance, like it should make the problem impossible.

Q: What is "Poverty Bayes," and what did it cost to train a two-million-parameter Bayesian model?

A: Poverty Bayes was Chris’ experiment in seeing how cheaply a large Bayesian model could be trained using modern cloud infrastructure. He fit a hierarchical logistic regression with close to two million parameters, using PyMC's Hamiltonian Monte Carlo on a single A100 GPU rented through Modal, a serverless platform that deploys a Python script straight to GPU hardware with almost no setup. He'd originally guessed it would cost around five dollars, the price of a Big Mac, but the real bill came in an order of magnitude lower. A model that would take a Gibbs sampler weeks to run, and that once required a research lab's dedicated GPU, now costs pocket change and a few minutes of setup.

Q: What's the current bottleneck in Bayesian-at-scale tooling?

A: Chris argues the software has largely caught up: PyMC's JAX backend and NumPyro make GPU-accelerated Bayesian modeling work out of the box for most problems. What's missing is common knowledge. Companies are clearly running large Bayesian models in production, but the results stay behind corporate firewalls. Chris’ proposal is a community benchmark effort: which frameworks handle a million-parameter Markov random field on a given GPU out of the box, since this kind of expensive, slow-running benchmark is a poor fit for standard CI pipelines but valuable for the field to know.

Chapters:

22:57 When does GPU acceleration actually pay off for a Bayesian model?

26:33 What did it cost to train a two-million-parameter model on Modal?

30:36 What happened when Chris asked 200 different LLMs to flip a coin?

34:50 Where do Bayesian ideas show up in the agentic AI systems Chris builds at Nvidia?

40:16 Are statisticians being made obsolete by large language models?

41:19 How does putting a Gaussian process on unknown coordinates fix noisy data in mineral prospecting?

58:05 What is Chris looking forward to working on next?

Thank you to my Patronsfor making this episode possible!

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Today's clip is from episode 161, featuring Luigi Acerbi. In this conversation, Luigi explains one of the biggest engineering bottlenecks facing transformer-based probabilistic models—and how his group found a way around it.

The core challenge is that many inference models treat data as an unordered set, making them naturally permutation invariant. That's statistically elegant, but computationally painful: every time a new data point arrives, the model has to recompute attention over the entire dataset from scratch, preventing the kind of KV caching that makes modern language models so efficient.

Luigi walks through his team's solution: a hybrid architecture that keeps the original context fully set-based while introducing a causal-attention buffer for newly arriving data. The result is dramatically faster inference- up to 100× faster in some settings - opening the door to applications like reinforcement learning, active data acquisition, and, ultimately, Luigi's long-term vision of a foundation model for Bayesian inference.

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work

Support & Resources
→ Support the show on Patreon
→ Bayesian Modeling Course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work

Takeaways:
Q: What is Variational Bayesian Monte Carlo (VBMC) and how is it different from Bayesian optimization?
A: VBMC borrows the machinery of Bayesian optimization but aims at a different target. Bayesian optimization fits a Gaussian process surrogate to an expensive function and uses it to hunt for the optimum. VBMC instead treats the log-posterior as the function to model, evaluates it at a few carefully chosen points, and keeps the whole reconstructed shape rather than just its peak. That gives you the full posterior, not a single best-fit value. Where MCMC might need tens of thousands to millions of evaluations, VBMC often reconstructs a good posterior approximation from a few hundred, which matters when each evaluation is slow.

Q: When should you reach for PyVBMC, and when is it the wrong tool?
A: Two symptoms tell you PyVBMC might help. First, speed: if a single evaluation of your log density takes on the order of a second, running MCMC over tens of thousands of evaluations becomes painful, and PyVBMC's few-hundred-evaluation budget pays off. Second, dimensionality: because it leans on a Gaussian process surrogate, it works well up to roughly 10 to 15 parameters and degrades beyond that. If your model already runs fine in Stan or PyMC, you do not need it. It shines for expensive, low-dimensional models common in science and engineering, where you are modeling a process rather than composing nice distributions.

Full takeaways here

Chapters:
00:18:13 What is Variational Bayesian Monte Carlo (VBMC) and how does it differ from Bayesian optimization?
00:30:21 When should you use VBMC versus BADS in practice?
00:31:20 What is Bayesian Adaptive Direct Search (BADS) and how does its hybrid optimization strategy work?
00:39:18 What are neural processes, and why are transformers a natural neural process architecture?
00:45:54 What is the Amortized Conditioning Engine (ACE) and what problem does it unify?
00:55:42 What do PriorGuide and the new autoregressive buffer paper solve for amortized inference?
01:02:03 How does the new autoregressive buffer speed up predictions in transformer probabilistic models?
01:06:11 What is Luigi Acerbi's vision for a foundation model for inference?
01:09:26 What is ALINE and how does it add active data acquisition to amortized inference?
01:12:43 How does Luigi Acerbi connect LLM agents, Bayesian decision theory, and the nature of intelligence?
01:18:44 For a PyMC, Stan, or NumPyro user, where should you start with VBMC, BADS, or BayesFlow?

Thank you to my Patronsfor making this episode possible!

Links from the show here

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work

Takeaways:
Q: What's the difference between Bayesian statistics and Bayesian epistemology?
A: Bayesian statistics uses Bayes' theorem on actual data: you put a prior over parameters, combine it with a likelihood, and the data is allowed to tell you your model is wrong. Vaden loves it. Bayesian epistemology, in his tongue-in-cheek phrase, is "Bayesian statistics minus the statistics" - taking Bayes' theorem as a general account of how anyone should reason under uncertainty, including about events where there is nothing to count. The first is falsifiable and grounded; the second, he argues, lets people attach authoritative-sounding numbers to pure belief.

Q: Why is it a problem to put a probability on a one-off future event like human extinction?
A: Because there are no statistics behind it. Vaden's trigger example is Toby Ord's The Precipice, where a data-derived probability (supervolcanoes per millennium) is placed side by side with a probability of extinction-by-superintelligence that came from no data at all. His reaction is the statistician's first instinct: where are the numbers coming from, and what could ever make them come out differently? A subjective degree of belief is fine as a hunch. The trouble starts when it is communicated as though it were an objective, data-grounded frequency.

Q: What does Vaden Masrani actually like about Bayesian statistics?
A: The freedom to encode domain knowledge as a prior and have the result respect common sense - estimating an average human height, you can rule out zero and a hundred feet before seeing a single measurement. But the part he keeps stressing is falsifiability: you fit the model, compare it to data, and the data can tell you the model was bad. That contact with reality is exactly what makes the statistics legitimate and what the epistemology lacks. On Bayesian-versus-frequentist for engineering problems, he says he has no dog in the fight -- both are useful, and any working statistician uses both.

Full takeaways here

Chapters:

00:24:01 What's the difference between Bayesian statistics and Bayesian epistemology?
00:33:12 How can Bayesian epistemology lead to bad real-world decisions?
00:36:36 Is Bayesian or frequentist statistics better for real-world problems?
00:39:31 What is the problem of induction, and how does Bayesian epistemology try to solve it?
00:43:50 What are the main logical problems with Bayesian epistemology?
00:48:40 What is Popper's critical rationalism, and how does falsifiability fit in?
00:52:31 How does critical rationalism work when you can't run a clean experiment?
01:15:03 Why should you treat criticism as a gift, even when it hurts?
01:19:54 How do Stoicism and equanimity help you handle criticism?
01:23:19 Why does critical rationalism apply to everyday life, not just science?

Thank you to my Patronsfor making this episode possible!

Links from the show here

Today's clip is from Episode 158featuring Stefan Radev. In this conversation, Alex Andorra and Stefan break down a core argument from their paper: Bayesian statistics has never been more computational than it is now, and simulation is the thread that ties the whole workflow together.

Stefan parcellates the Bayesian workflow into four stages, and this clip covers the first two. Stage one is model specification, where the workflow community has long recommended prior predictive checks. You can do this informally, just running simulations from your model and eyeballing whether the output meets your expectations, or formally, à la Michael Betancourt, by pushing your model's high-dimensional output through a transformation into a low-dimensional, interpretable space and checking it against reality.

The punchline: a surprising number of models can be discarded before you've even seen real data, yet Stefan notes these checks remain underused in practice.

Stage two is model verification, where the question shifts to whether your inferences are well calibrated. This is the territory of simulation-based calibration and parameter recovery studies, classic tools that have always carried a steep computational price. You simulate thousands of synthetic datasets and run inference on every single one, which is exactly why these checks are so often skipped in papers, even though doing one well can be a contribution in its own right.

Here's where amortized simulation-based inference changes the math entirely. Checks that used to take days now take seconds, and instead of laboriously running inference dataset by dataset, you get millions of posterior samples essentially for free. The calibration checks that the field has always known it should be doing finally become cheap enough to actually do.

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Today's clip is from episode 159 featuring Matthijs Hollanders. In this conversation, Alex and Matthijs dig into a deceptively practical question: when you're modeling wildlife across space and time with Gaussian Processes, how do you keep the math from becoming computationally unbearable - and what does good engineering actually look like in the field?

Matthijs explains that for most real camera trapping datasets, exact GPs still hold up fine. The reason is less about clever math and more about ecological reality: researchers are usually resource-constrained, so datasets tend to be a few hundred sites, not thousands.

And when datasets do get large, they're rarely one giant connected grid - they're clusters of independent regions. That structure is exploitable. Run a separate, smaller GP per region, share the hyperparameters, and you avoid building the massive covariance matrix that makes exact GPs expensive in the first place.

But the more interesting thread is where this is heading. Alex introduces Hilbert Space Gaussian Processes (HSGPs) - an approximation that makes compute time nearly linear in dataset size, rather than cubic. The catch, as Matthijs points out, is that approximations aren't always better: if your dataset isn't large enough to be in the regime where the approximation accuracy kicks in, you're better off with the exact GP and its mathematical guarantees. The rule of thumb is simple - if you can use the vanilla GP, just do it.

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work

Support & Resources
→ Support the show on Patreon
→ Bayesian Modeling Course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work

Takeaways:

Q: What is a Bayesian occupancy model and what problem does it solve?
A: An occupancy model accounts for the fact that you don't always detect a species when surveying for it, especially when the species is rare. A naive count of where you found it underestimates true occupancy. The model adds a repeated-measures component: you visit each site multiple times, and from the pattern of detections vs. non-detections it estimates a detection probability. Matthijs framed it as a zero-inflation structure where the zero-inflation happens at the site level rather than the observation level -- which keeps the model conceptually simple, just a standard GLM with a Bernoulli “is the species here at all?” stacked on top of a detection-rate process.

Q: What are Automated Recording Units and why don't traditional occupancy models handle them well?
A: ARUs are camera traps and acoustic monitors that record continuously over deployment periods of days, weeks, or months. The data they produce isn't a sequence of discrete human-led surveys; it's a continuous-time observation stream. Traditional occupancy models were designed for the discrete case -- a human visits a site, records yes or no, goes home. With ARUs, the question becomes how to bin or threshold the continuous data without losing the richer signal it actually contains.

Q: When should you not reach for occARU?

A: When your dataset is large and your survey interval is fine-grained. The bottleneck is Stan's fitting speed -- years of daily count data across many sites will fit slowly. The workaround is to bin coarser (weekly or monthly), which doesn't hurt occupancy estimation at all and only loses some detection-rate resolution. If you're only interested in occupancy, big grouping windows are fine.

Full takeaways here

Chapters:

00:12:14 What is an occupancy model and what problem does it solve?
00:16:16 What are Automated Recording Units and why do they need different models?
00:18:45 What is the occARU R package and why does it exist?
00:23:55 Why does occARU model counts directly rather than binary detection?
00:26:38 What does multi-species hierarchical modeling with Gaussian processes look like?
00:32:22 How does occARU implement Gaussian processes efficiently?
00:41:01 Why are Gaussian processes such a powerful but tricky modeling tool?
00:44:11 What is variance decomposition with global-local shrinkage priors?
00:49:02 How does occARU leverage recent Stan features for zero-sum constraints?
00:57:37 When does within-chain parallelization actually help?
01:01:30 How does Monte Carlo integration reduce high Pareto-k values?
01:15:27 When does occARU underperform and what's on the roadmap?

Thank you to my Patronsfor making this episode possible!

Links from the show here.

Today's clip is from episode 158featuring Stefan Radev. In this conversation, Alex and Stefan explore a genuinely fascinating problem: how do you turn an expert's intuition into a mathematically valid prior distribution - and can AI help automate that process?

Alex explains that prior elicitation is essentially a translation problem. Experts don't walk around thinking in probability distributions - their knowledge lives in intuitions, rules of thumb, and rough ranges. The challenge is converting that into something a Bayesian model can actually use.

The traditional approach? Ask an expert for quantiles or a mean, then parameterize your prior with hyperparameters and simulate until the model-implied quantities match what the expert described. If your pipeline is differentiable end-to-end, you use gradient descent. If not, you fall back to something like Bayesian optimization. Either way, you're iterating toward a prior that genuinely reflects expert knowledge - not just a convenient assumption.

But the really exciting part is what came next. In a follow-up paper, they pushed this further: instead of optimizing within a fixed parametric family (say, a Gaussian), they replaced the prior entirely with a normalizing flow - a flexible generative network - and ran the same procedure. No assumed distribution family. Just let the data and the expert's knowledge shape the prior from scratch.

The catch? More flexibility means more non-identifiability and stability headaches. But the direction is clear: a fully automated, end-to-end pipeline for building priors from non-probabilistic expert knowledge. And in 2026, that pipeline could theoretically be driven by an agent.

Get the full discussion here

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work

Support & Resources
→ Support the show on Patreon
→ Bayesian Modeling Course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work

Takeaways:

Q: Why are prior predictive checks so underused in practice, and how do simulations help?
A: They're underused because researchers don't always think to run them before seeing data -- but also because doing them rigorously (in the style Michael Betancourt advocates, with prior push-forward checks on interpretable summaries) takes effort. Simulations make it cheap to generate thousands of “what-if world” datasets from your model and check whether they look plausible, catching bad priors before you ever touch real data.

Q: How can generative AI help with prior elicitation?
A: Rather than forcing a domain expert to choose a distributional family and parameterize it, you can use a generative model to translate their qualitative knowledge directly into a prior. The expert describes what realistic data should look like; the generative model produces synthetic datasets matching that description; those datasets are used to fit a prior distribution. It removes the assumption that experts can think in terms of parameters and replaces it with the more natural question: does this look like your data?

Q: What would a foundation model for Bayesian inference actually look like?
A: Stefan's bet is that it won't be a fine-tuned general LLM. The right analogy is chess: you don't fine-tune GPT to play chess, you teach it when to call Stockfish. For Bayesian inference, you'd want a semantic layer – an LLM that understands the analysis goal – calling specialized numerical engines (MCMC samplers, amortized inference networks) that do the actual computation. Agent skills are already a step in this direction; the longer-term vision is engines that have been trained from scratch to generalize across large families of models and priors.

Full takeaways here.

Chapters:
00:00 How does amortized inference fit into modern Bayesian workflows?
06:01 What role do simulations play across the full Bayesian workflow?
12:12 How do you elicit priors from a domain expert who doesn't think in distributions?
19:01 What would a foundation model for Bayesian inference actually look like?
35:32 What is self-consistency in amortized inference and why does it matter?
39:22 How does semi-supervised learning improve simulation-based inference?
43:16 Why is sensitivity analysis so important yet so underused in Bayesian practice?
47:40 What is multiverse analysis and how does it change how we report Bayesian results?
51:32 How does amortized inference make sensitivity and multiverse analysis affordable?
01:02:47 How do amortized inference and classical MCMC complement each other?
01:10:08 What are the next major directions for BayesFlow and amortized inference research?

Thank you to my Patronsfor making this episode possible!

Links from the show here.

Today's clip is from Episode 157 featuring Stefan Radev. In this conversation, Alex and Stefan dig into one of the hardest open problems in simulation-based inference — hierarchical models.

The core idea: when you move from flat to hierarchical models, you're no longer estimating one set of parameters. You have local parameters that vary by location (or subject, or city) and global parameters that capture what's shared across all of them. And you don't just want each separately — you want the full joint posterior, because that's where the Bayesian magic of shrinkage actually lives.

Stefan builds the problem from the ground up. Start with the simplest hierarchical case: a two-level model. He uses electoral forecasting in France as the example — cities nested inside departments nested inside the whole country.

Now your simulator has to cover all three levels. If that simulator is slow (think: brain emulators, minutes per sample), scaling to hundreds of groups becomes completely intractable. Memory issues, specialized network requirements, the works.

The key insight: this problem has structure you can exploit. The joint posterior factorizes in a particularly nice way — each local parameter depends on its own local data and on the global parameters. That means instead of cramming everything into one giant high-dimensional vector and hoping a neural network figures it out, you can decompose the problem. Estimate local parameters conditioned on local data and the globals. Use composition.

The takeaway: hierarchical models aren't just "harder flat models" - they have a geometry that demands a different architecture. Respecting that structure is what makes amortized inference scale.

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work

Support & Resources
→ Support the show on Patreon
→ Bayesian Modeling Course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work

Takeaways:

Q: What is simulation-based inference and what does "sim-to-real" mean?
A: Simulation-based inference (SBI) uses a mechanistic simulator as an epistemic tool: you train a neural network on a large number of labeled simulations and then deploy it on real, unlabeled data. The "sim-to-real" framing captures the key asymmetry -- your network never sees real data during training, only simulations, but it generalizes to real observations at inference time. This is the opposite of the more common "synthetic-for-ML" approach, where fake data is used purely to augment real training data.

Q: What is the amortized inference agent skill and what does it do?
A: It's an open-source AI agent skill, co-developed by Stefan and Alexandre, that teaches an AI coding agent to run a complete, state-of-the-art amortized inference workflow. Because amortized inference is recent enough that it's underrepresented in LLM training data, vanilla agents tend to get it wrong. The skill injects the right methodology: it guides the agent to set up the simulator, choose the right network architecture, run a pilot, train with appropriate diagnostics, and produce an actionable report -- without the user needing to know the details.

Q: What is calibration coverage and why should you never skip it?
A: Calibration coverage tells you whether your posterior uncertainty is honest -- whether your credible intervals actually contain the true parameter at the right frequency. A model can show poor parameter recovery yet still be well-calibrated (because it's falling back on the prior), or it can appear to recover parameters while being poorly calibrated. Running calibration diagnostics both in-sample and out-of-sample is especially revealing for hierarchical models, which often appear to underfit in-sample but generalize much better out-of-sample thanks to shrinkage.

Full takeaways here

Chapters:
00:00:00 How does amortized inference fit into the Bayesian workflow?
00:12:03 What does "sim-to-real" mean in simulation-based inference?
00:15:57 Why is amortized inference particularly suited to psychology and neuroscience?
00:21:51 What is the amortized inference agent skill?
00:39:00 What is calibration coverage and how do you interpret it?
00:41:50 How do you decide what to do next after your first training run?
00:44:53 How do actionable insights make Bayesian workflows more usable?
00:49:08 What are the unique challenges of hierarchical models in amortized inference?
01:00:51 What is the current state of BayesFlow's support for hierarchical models?
01:05:00 What are the main failure modes of amortized inference and how do you handle model misspecification?

Thank you to my Patronsfor making this episode possible!

Links from the show

Today's clip is from Episode 156 featuring Adam Foster. In this conversation, Adam explains Expected Information Gain (EIG) -the scoring function at the heart of optimal Bayesian experimental design.

The core idea: when designing an experiment, you need a way to compare possible designs and pick the best one. EIG is that score - it tells you how much information you expect to gain about your model parameters from a given design. The higher the EIG, the better the design.

Adam builds intuition for EIG from two directions that sound completely different but lead to the same place. First, the Bayesian angle: simulate datasets from your prior predictive distribution, run inference on each, measure how much uncertainty dropped, and average across datasets. Second, a classic puzzle - the 12 prisoners balance scale problem - where the best weighing strategy turns out to be the one that makes all three outcomes (tip left, tip right, balance) equally likely. This maximizes outcome entropy, which is exactly what EIG does: it steers you toward designs where every possible result narrows down your hypotheses as fast as possible.

The takeaway: good experimental design isn't about intuition or convention - it's about making your data work as hard as possible, and EIG gives you a rigorous way to do that.

Get the full discussion here

Support & Resources
→ Support the show on Patreon
→ Bayesian Modeling Course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work

Support & Resources
→ Support the show on Patreon
→ Bayesian Modeling Course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work

Takeaways

Q: What is Bayesian experimental design and what problem does it solve?
A: It's the practice of using a Bayesian model to decide how to collect data before you collect it. Most statistical thinking starts with a fixed dataset. Bayesian experimental design sits upstream -- you have control over experimental parameters (which questions to ask, which reagents to mix, which conditions to test) and you want to choose them optimally. The Bayesian angle is to ask: what new data would most reduce my current uncertainty?

Q: When should you actually use Bayesian experimental design?
A: When two conditions hold: you have active control over how data is collected (not just passive observation), and you have a Bayesian model whose prior predictive distribution gives a reasonable picture of what typical data might look like. It's especially valuable when data collection is expensive or irreversible -- when the "committal step" of running an experiment has real cost, it's worth doing the analysis first.

Q: What is expected information gain (EIG) and why is it central to Bayesian experimental design?
A: EIG is the score you assign to a candidate experimental design -- the amount of information you expect to gain about your model parameters by running an experiment with that design. You compute it by simulating datasets from your prior predictive, doing Bayesian inference on each, and averaging how much the uncertainty decreased. What's remarkable is that you can derive the same quantity from two completely different starting points -- reducing parameter uncertainty, or maximizing outcome uncertainty while correcting for noise - and arrive at the same formula. That convergence is why EIG keeps being re-discovered independently across fields.

Full takeaways here

Chapters:

00:00 What is Bayesian experimental design and why does it matter?

00:06:02 What problem does Bayesian experimental design actually solve?

00:08:54 When should practitioners use Bayesian experimental design?

00:12:00 Is Bayesian experimental design changing how scientists work in practice?

00:15:04 What are the limitations of Bayesian experimental design?

00:17:55 What is expected information gain (EIG) and how does it work?

00:21:05 How do you compute expected information gain in practice?

00:23:48 What is active learning and how does it connect to Bayesian experimental design?

00:41:02 What is active learning by disagreement?

00:48:57 What is deep adaptive design and when should you00: use it?

00:56:02 How is Bayesian experimental design applied in protein dynamics and quantum chemistry?

01:01:58 What does a practical Bayesian experimental design workflow look like?

Thank you to my Patronsfor making this episode possible!

Links from the show

Today's clip is from Episode 152 of the podcast, featuring Daniel Saunders. In this conversation, Daniel explores how Bayesian decision theory handles real-world risk aversion beyond the textbook maximum expected utility framework.

The key insight: classical Bayesian decision theory assumes risk neutrality, but in practice, people and businesses are risk-averse. Using a pricing optimization example, Daniel shows how uncertainty varies dramatically across price points—lower prices have predictable demand, while higher prices create wide uncertainty in profits. This asymmetry matters when you want safer decisions.

Daniel introduces exponential utility functions—a technique from economics that models diminishing returns on money. By adjusting a risk-aversion parameter, you can see how increasing risk aversion shifts optimal decisions away from high-uncertainty, high-profit scenarios toward more predictable outcomes.

The broader lesson: optimal decision-making requires separating the modeling process from the decision process, allowing you to build in constraints and risk adjustments that pure expected utility maximization would miss.

Get the full discussion here

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Support & Resources
→ Support the show on Patreon
→ Bayesian Modeling Course (first 2 lessons free):

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work

Takeaways:

Q: Why is bridging deep learning and probabilistic programming so important?
A: Deep learning is extraordinarily good at fitting complex functions, but it throws away uncertainty. Probabilistic programming keeps uncertainty explicit throughout. Combining the two – as in inference compilation – lets you get the expressiveness of neural networks while still doing proper Bayesian inference.

Q: What is inference compilation and how does it relate to amortized inference?
A: Amortized inference is the general idea of training a model upfront so you don't have to run expensive inference from scratch every single time. Inference compilation is a specific form of amortized inference where a neural network is trained to propose good posterior samples for a given probabilistic program – essentially learning to do inference rather than computing it fresh each query.

Q: What is PyProb and what problems does it solve?
A: PyProb is a probabilistic programming library designed specifically to support amortized inference workflows. It lets you write probabilistic models in Python and then train inference networks on top of them, making methods like inference compilation practical for real-world simulators and scientific models.

Full takeawayshere.

Chapters:

00:00:00 Introduction to Bayesian Inference and Its Barriers
00:03:51 Andreas Munch's Journey into Statistics
00:10:09 Bridging the Gap: Bayesian Inference in Real-World Applications
00:15:56 Deep Learning Meets Probabilistic Programming
00:22:05 Understanding Inference Compilation and Amortized Inference
00:28:14 Exploring PyProb: A Tool for Amortized Inference
00:33:55 Probabilistic Surrogate Networks and Their Applications
00:38:10 Building Surrogate Models for Probabilistic Programming
00:45:44 The Challenge of Bayesian Inference in Enterprises
00:52:57 Communicating Uncertainty to Stakeholders
01:01:09 Democratizing Bayesian Inference with Evara
01:06:27 Insurance Pricing and Latent Variables
01:16:41 Modeling Uncertainty in Predictions
01:20:29 Dynamic Inference and Decision-Making
01:23:17 Updating Models with Actual Data
01:26:11 The Future of Bayesian Sampling in Excel
01:31:54 Navigating Business Challenges and Growth
01:36:40 Exploring Language Models and Their Applications
01:38:35 The Quest for Better Inference Algorithms
01:41:01 Dinner with Great Minds: A Thought Experiment

Thank you to my Patronsfor making this episode possible!

Links from the showhere.

Today's clip is fromEpisode 154 of the podcast, with Thomas Pinder.

In this conversation, Thomas Pinder explains how Bayesian methods naturally lend themselves to causal modeling, and why that matters for real-world business decisions. The key insight is that causal questions in industry are rarely black and white: instead of a single treatment effect, you get a full posterior distribution, credible intervals, and the ability to communicate the probability that an effect is positive, which is far more useful to stakeholders than a p-value.

Thomas then dives into Bayesian Synthetic Control, a reframing of the classic synthetic control method from a constrained optimization problem into a Bayesian regression problem. Rather than optimizing weights on a simplex, you place a Dirichlet prior on the regression coefficients, which turns out to be not just mathematically elegant but practically richer: you can express prior beliefs about how many control units are informative, set the concentration parameter accordingly, or let a gamma hyperprior on that parameter let the data decide. The result is a more flexible, less fragile counterfactual, implemented cleanly in PyMC or NumPyro.

Get the full discussion here

Support & Resources
→ Support the show on Patreon: https://www.patreon.com/c/learnbayesstats
→ Bayesian Modeling Course (first 2 lessons free): https://topmate.io/alex_andorra/1011122

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

• Support & get perks!

• Bayesian Modeling course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Takeaways:

Q: Why was GPJax created and how does it benefit researchers?
A: GPJax was developed to provide a high-performance, flexible framework for Gaussian processes (GPs) within the JAX ecosystem. It allows researchers to move beyond black-box implementations and easily experiment with custom kernels and model structures while leveraging JAX’s automatic differentiation and GPU acceleration.

Q: What are the primary advantages of using Gaussian processes for data modeling?
A: Gaussian processes are highly effective at modeling complex, nonlinear relationships in data. Unlike many machine learning methods that only provide a point estimate, GPs offer built-in uncertainty quantification, which is essential for understanding the reliability of predictions in research and industry.

Q: How does the GPJax and NumPyro integration enhance probabilistic modeling?
A: The integration allows users to treat GPJax models as components within a larger NumPyro probabilistic program. This combination enables the use of advanced sampling techniques like NUTS (No-U-Turn Sampler), making it easier to build and fit complex hierarchical models that include Gaussian processes.

Q: What are the main challenges when applying Gaussian processes to high-dimensional data?
A: High-dimensional data significantly complicates GP modeling due to the curse of dimensionality and the cubic scaling of computational costs. In high dimensions, defining meaningful distance metrics for kernels becomes harder, often requiring specialized techniques like sparse GPs or dimensionality reduction to remain tractable.

Full takeaways here!

Chapters:

11:40 What is GPJax and how does it simplify Gaussian Process modeling?
15:48 How are Bayesian methods used for experimentation and causal inference in industry?
18:40 How do you implement Bayesian Synthetic Control?
32:17 What is Bayesian Synthetic Difference-in-Differences?
39:44 What are the research applications and supported methods for the GPJax library?
45:47 What are the primary software and computational bottlenecks when scaling Gaussian Processes?
49:02 What are the real-world industrial applications of Gaussian Process models?
54:36 How is Bayesian modeling applied to soccer and sports analytics?
58:43 What is the future development roadmap for the GPJax ecosystem?
01:05:37 What is Impulso and how does it integrate into a Bayesian modeling workflow?
01:13:42 How do you balance Bayesian computational overhead with industrial latency requirements?
01:20:26 Why is there optimism that scalable Bayesian methods for causal inference are now within reach?

Thank you to myPatronsfor making this episode possible!

Links from the show here!

• Support & get perks!

• Bayesian Modeling course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Takeaways:

Q: Is generosity a natural human trait?
A: Yes, generosity is hardwired in our brains and is essential for social interaction.

Q: Why do people say they care about causes but not act on it?
A: There is often a disconnect between stated care for causes and actual action. Understanding the conditions under which generosity aligns with a person's identity is crucial for bridging this gap.

Q: How should fundraising efforts be approached?
A: Fundraising should primarily focus on belief updating rather than mere persuasion.

Q: What are the benefits of being generous?
A: Generosity has significant mental and physical health benefits, as the brain's reward systems activate when we give, making us feel good.

Q: How do our beliefs relate to our actions?
A: Our beliefs about ourselves strongly influence our actions and decisions, including our decision to be generous.

Q: Can generosity impact a community?
A: Yes, generosity can be a powerful tool for improving community dynamics.

Q: How can technology like AI assist institutions with donors?
A: AI could help institutions remember donors better, improving the donor-institution relationship.

Chapters:

00:00 What's the role of Behavioral Science inPhilanthropy
19:57 What is The Neuroscience of Generosity?
24:40 How can we best understand Donor Decision-Making?
32:14 How can we achieve reframe Beliefs and Actions?
35:39 What is the role of Identity in Habit Formation?
38:06 What is the Generosity Gap in Philanthropy?
45:06 How can we reduce Friction in Donation Processes?
48:27 What is the role of AI and Trust in Nonprofits?
52:11 How can we build Predictive Models for Donor Behavior?
55:41 What is the role of Empathy in Sales and Stakeholder Engagement?
01:00:46 How can we best align ideas with Stakeholder Beliefs?
01:02:06 How can we explore Generosity and Memory?

Thank you to myPatronsfor making this episode possible!

Links from the show:

Today's clip is from Episode 152 of the podcast, with Daniel Saunders.

In this conversation, Daniel Saunders explains how to incorporate risk aversion into Bayesian price optimization. The key insight is that uncertainty around expected profit is asymmetric across price points, low prices yield more predictable (if modest) returns, while high prices introduce much wider uncertainty. Rather than simply maximizing expected profit, you can pass profit through an exponential utility function that models diminishing returns, a well-established idea from economics.

This adds an adjustable risk aversion parameter to the optimization: as risk aversion increases, the model shifts toward more conservative price recommendations, trading off potentially large but uncertain gains for outcomes with tighter, more reliable distributions.

Get the full discussion here

• Join this channel to get access to perks:
https://www.patreon.com/c/learnbayesstats

• Intro to Bayes Course (first 2 lessons free): https://topmate.io/alex_andorra/503302
• Advanced Regression Course (first 2 lessons free): https://topmate.io/alex_andorra/1011122

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

• Support & get perks!

• Proudly sponsored by PyMC Labs!

• Intro to Bayes and Advanced Regression courses (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Chapters:

00:00 The Importance of Decision-Making in Data Science

06:41 From Philosophy to Bayesian Statistics

14:57 The Role of Soft Skills in Data Science

18:19 Understanding Decision Theory Workflows

22:43 Shifting Focus from Accuracy to Business Value

26:23 Leveraging PyTensor for Optimization

34:27 Applying Optimal Decision-Making in Industry

40:06 Understanding Utility Functions in Regulation

41:35 Introduction to Obeisance Decision Theory Workflow

42:33 Exploring Price Elasticity and Demand

45:54 Optimizing Profit through Bayesian Models

51:12 Risk Aversion and Utility Functions

57:18 Advanced Risk Management Techniques

01:01:08 Practical Applications of Bayesian Decision-Making

01:06:54 Future Directions in Bayesian Inference

01:10:16 The Quest for Better Inference Algorithms

01:15:01 Dinner with a Polymath: Herbert Simon

Thank you to myPatronsfor making this episode possible!

Links from the show:

Today's clip is fromEpisode 151 of the podcast, with Jonas Arruda

In this conversation, Jonas Arruda explains how diffusion models generate data by learning to reverse a noise process. The idea is to start from a simple distribution like Gaussian noise and gradually remove noise until the target distribution emerges. This is done through a forward process that adds noise to clean parameters and a backward process that learns how to undo that corruption. A noise schedule controls how much noise is added or removed at each step, guiding the transformation from pure randomness back to meaningful structure.

Get the full discussion here

• Join this channel to get access to perks:
https://www.patreon.com/c/learnbayesstats

• Intro to Bayes Course (first 2 lessons free): https://topmate.io/alex_andorra/503302
• Advanced Regression Course (first 2 lessons free): https://topmate.io/alex_andorra/1011122

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

• Support & get perks!

• Proudly sponsored by PyMC Labs!

• Intro to Bayes and Advanced Regression courses (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Chapters:
00:00 Exploring Generative AI and Scientific Modeling
10:27 Understanding Simulation-Based Inference (SBI) and Its Applications
15:59 Diffusion Models in Simulation-Based Inference
19:22 Live Coding Session: Implementing Baseflow for SBI
34:39 Analyzing Results and Diagnostics in Simulation-Based Inference
46:18 Hierarchical Models and Amortized Bayesian Inference
48:14 Understanding Simulation-Based Inference (SBI) and Its Importance
49:14 Diving into Diffusion Models: Basics and Mechanisms
50:38 Forward and Backward Processes in Diffusion Models
53:03 Learning the Score: Training Diffusion Models
54:57 Inference with Diffusion Models: The Reverse Process
57:36 Exploring Variants: Flow Matching and Consistency Models
01:01:43 Benchmarking Different Models for Simulation-Based Inference
01:06:41 Hierarchical Models and Their Applications in Inference
01:14:25 Intervening in the Inference Process: Adding Constraints
01:25:35 Summary of Key Concepts and Future Directions

Thank you to myPatronsfor making this episode possible!

Links from the show:

- Come meet Alex at the Field of Play Conference in Manchester, UK, March 27, 2026!
- Jonas's Diffusion for SBI Tutorial & Review (Paper & Code)
- The BayesFlow Library
- Jonas on LinkedIn
- Jonas on GitHub
- Further reading for more mathematical details: Holderrieth & Erives
- 150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik
- 107 Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt

• Support & get perks!

• Proudly sponsored by PyMC Labs!

• Intro to Bayes and Advanced Regression courses (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Chapters:

00:00 Scaling Bayesian Neural Networks
04:26 Origin Stories of the Researchers
09:46 Research Themes in Bayesian Neural Networks
12:05 Making Bayesian Neural Networks Fast
16:19 Microcanonical Langevin Sampler Explained
22:57 Bottlenecks in Scaling Bayesian Neural Networks
29:09 Practical Tools for Bayesian Neural Networks
36:48 Trade-offs in Computational Efficiency and Posterior Fidelity
40:13 Exploring High Dimensional Gaussians
43:03 Practical Applications of Bayesian Deep Ensembles
45:20 Comparing Bayesian Neural Networks with Standard Approaches
50:03 Identifying Real-World Applications for Bayesian Methods
57:44 Future of Bayesian Deep Learning at Scale
01:05:56 The Evolution of Bayesian Inference Packages
01:10:39 Vision for the Future of Bayesian Statistics

Thank you tomy Patronsfor making this episode possible!

Come meet Alex at the Field of Play Conference in Manchester, UK, March 27, 2026!

Links from the show:

David Rügamer:
* Website
* Google Scholar
* GitHub

Emanuel Sommer:
* Website
* GitHub
* Google Scholar

Jakob Robnik:
* Google Scholar
* GitHub
* Microcanonical Langevin paper
* LinkedIn

Today’s clip is from episode 149 of the podcast, with Alana Karen.

This conversation explores the evolving landscape of technology, particularly in Silicon Valley, focusing on the cultural shifts due to mass layoffs, the debate over remote work, and the impact of AI on job roles and priorities. The discussion highlights the importance of adapting to these changes and preparing for the future by developing complex skills that AI cannot easily replicate.

Get the full discussion here!

• Join this channel to get access to perks:
https://www.patreon.com/c/learnbayesstats

• Intro to Bayes Course (first 2 lessons free): https://topmate.io/alex_andorra/503302
• Advanced Regression Course (first 2 lessons free): https://topmate.io/alex_andorra/1011122

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

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• Proudly sponsored by PyMC Labs!

• Intro to Bayes and Advanced Regression courses (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Chapters:

11:37 The Hard Tech Era
21:08 The Shift in Tech Work Culture
28:49 AI's Impact on Job Security and Work Dynamics
34:33 Adapting to AI: Skills for the Future
45:56 Understanding AI Models and Their Limitations
47:25 The Importance of Diversity in AI Development
54:34 Positioning Technical Talent for Job Security
57:58 Building Resilience in Uncertain Times
01:06:33 Recognizing Diverse Ambitions in Career Progression
01:12:51 The Role of Managers in Employee Retention
01:26:55 Solving Complex Problems with AI and Innovation

Thank you tomy Patronsfor making this episode possible!

Links from the show:

Today’s clip is from episode 148 of the podcast, with Scott Berry.

In this conversation, Alex and Scott discuss emphasizing the shift from frequentist to Bayesian approaches in clinical trials.

They highlight the limitations of traditional trial designs and the advantages of adaptive and platform trials, particularly in the context of COVID-19 treatment.

The discussion provides insights into the complexities of trial design and the innovative methodologies that are shaping the future of medical research.

Get the full discussion here!

• Join this channel to get access to perks: https://www.patreon.com/c/learnbayesstats

• Intro to Bayes Course (first 2 lessons free): https://topmate.io/alex_andorra/503302

• Advanced Regression Course (first 2 lessons free): https://topmate.io/alex_andorra/1011122

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

• Support & get perks!

• Proudly sponsored by PyMC Labs. Get in touch and tell them you come from LBS!

• Intro to Bayes and Advanced Regression courses (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Chapters:

13:16 Understanding Adaptive and Platform Trials

25:25 Real-World Applications and Innovations in Trials

34:11 Challenges in Implementing Bayesian Adaptive Trials

42:09 The Birth of a Simulation Tool

44:10 The Importance of Simulated Data

48:36 Lessons from High-Stakes Trials

52:53 Navigating Adaptive Trial Designs

56:55 Communicating Complexity to Stakeholders

01:02:29 The Future of Clinical Trials

01:10:24 Skills for the Next Generation of Statisticians

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Giuliano Cruz, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Joshua Meehl, Javier Sabio, Kristian Higgins, Matt Rosinski, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık, Suyog Chandramouli, Guillaume Berthon, Avenicio Baca, Spencer Boucher, Krzysztof Lechowski, Danimal, Jácint Juhász, Sander and Philippe.

Links from the show:

Today’s clip is from episode 147 of the podcast, with Martin Ingram.

Alex and Martin discuss the intricacies of variational inference, particularly focusing on the ADVI method and its challenges. They explore the evolution of approximate inference methods, the significance of mean field variational inference, and the innovative linear response technique for covariance estimation.

The discussion also delves into the trade-offs between stochastic and deterministic optimization techniques, providing insights into their implications for Bayesian statistics.

Get the full discussion here.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

13:17 Understanding DADVI: A New Approach

21:54 Mean Field Variational Inference Explained

26:38 Linear Response and Covariance Estimation

31:21 Deterministic vs Stochastic Optimization in DADVI

35:00 Understanding DADVI and Its Optimization Landscape

37:59 Theoretical Insights and Practical Applications of DADVI

42:12 Comparative Performance of DADVI in Real Applications

45:03 Challenges and Effectiveness of DADVI in Various Models

48:51 Exploring Future Directions for Variational Inference

53:04 Final Thoughts and Advice for Practitioners

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël...

Today’s clip is from episode 146 of the podcast, with Ethan Smith.

Alex and Ethan discuss the application of Bayesian inference in high energy density physics, particularly in analyzing complex data sets. They highlight the advantages of Bayesian techniques, such as incorporating prior knowledge and managing uncertainties.

They also shares insights from an ongoing experimental project focused on measuring the equation of state of plasma at extreme pressures. Finally, Alex and Ethan advocate for best practices in managing large codebases and ensuring model reliability.

Get the full discussion here.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

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Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

14:31 Understanding High Energy Density Physics and Plasma Spectroscopy

21:24 Challenges in Data Analysis and Experimentation

36:11 The Role of Bayesian Inference in High Energy Density Physics

47:17 Transitioning to Advanced Sampling Techniques

51:35 Best Practices in Model Development

55:30 Evaluating Model Performance

01:02:10 The Role of High Energy Density Physics

01:11:15 Innovations in Diagnostic Technologies

01:22:51 Future Directions in Experimental Physics

01:26:08 Advice for Aspiring Scientists

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady,

Today’s clip is from episode 145 of the podcast, with Jordan Thibodeau.

Alexandre Andorra and Jordan Thibodeau discuss the transformative impact of AI on productivity, career opportunities in the tech industry, and the intricacies of the job interview process.

They emphasize the importance of expertise, networking, and the evolving landscape of tech companies, while also providing actionable advice for individuals looking to enhance their careers in AI and related fields.

Get the full discussion here.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Joshua Meehl, Javier Sabio, Kristian Higgins, Matt Rosinski, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık, Suyog Chandramouli and Guillaume Berthon.

Takeaways:

Today’s clip is from episode 144 of the podcast, with Maurizio Filippone.

In this conversation, Alex and Maurizio delve into the intricacies of Gaussian processes and their deep learning counterparts. They explain the foundational concepts of Gaussian processes, the transition to deep Gaussian processes, and the advantages they offer in modeling complex data.

The discussion also touches on practical applications, model selection, and the evolving landscape of machine learning, particularly in relation to transfer learning and the integration of deep learning techniques with Gaussian processes.

Get the full discussion here.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

08:44 Function Estimation and Bayesian Deep Learning

10:41 Understanding Deep Gaussian Processes

25:17 Choosing Between Deep GPs and Neural Networks

32:01 Interpretability and Practical Tools for GPs

43:52 Variational Methods in Gaussian Processes

54:44 Deep Neural Networks and Bayesian Inference

01:06:13 The Future of Bayesian Deep Learning

01:12:28 Advice for Aspiring Researchers

Today’s clip is from episode 143 of the podcast, with Christoph Bamberg.

Christoph shares his journey into Bayesian statistics and computational modeling, the challenges faced in academia, and the technical tools used in research.

Alex and Christoph delve into a specific study on appetite regulation and cognitive performance, exploring the implications of framing in psychological research and the importance of careful communication in health-related contexts.

Get the full discussion here.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

10:35 The Struggles of Bayesian Statistics in Psychology

22:30 Exploring Appetite and Cognitive Performance

29:45 Research Methodology and Causal Inference

36:36 Understanding Cravings and Definitions

39:02 Intermittent Fasting and Cognitive Performance

42:57 Practical Recommendations for Intermittent Fasting

49:40 Balancing Experimental Psychology and Statistical Modeling

55:00 Pressing Questions in Health Psychology

01:04:50 Future Directions in Research

Thank you to my Patrons for...

Get early access to Alex'snext live-cohort courses!

Today’s clip is from episode 142 of the podcast, with Gabriel Stechschulte.

Alex and Garbriel explore the re-implementation of BART (Bayesian Additive Regression Trees) in Rust, detailing the technical challenges and performance improvements achieved.

They also share insights into the benefits of BART, such as uncertainty quantification, and its application in various data-intensive fields.

Get the full discussion here.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

05:10 – From economics to IoT and Bayesian statistics

18:55 – Introduction to BART (Bayesian Additive Regression Trees)

24:40 – Re-implementing BART in Rust for speed and scalability

32:05 – Comparing BART with Gaussian Processes and other tree methods

39:50 – Strengths and limitations of BART

47:15 – Handling missing data and different likelihoods

54:30 – Variational inference and big data challenges

01:01:10 – Embedding BART into optimization and decision-making frameworks

01:08:45 – Open source, PyMC, and community support

01:15:20 – Advice for newcomers

01:20:55 – Future of BART, Rust, and probabilistic programming

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian...

Get early access to Alex'snext live-cohort courses!

Today’s clip is from episode 141 of the podcast, with Sam Witty.

Alex and Sam discuss the ChiRho project, delving into the intricacies of causal inference, particularly focusing on Do-Calculus, regression discontinuity designs, and Bayesian structural causal inference.

They explain ChiRho's design philosophy, emphasizing its modular and extensible nature, and highlights the importance of efficient estimation in causal inference, making complex statistical methods accessible to users without extensive expertise.

Get the full discussion here.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

05:53 Bridging Mechanistic and Data-Driven Models

09:13 Understanding Causal Probabilistic Programming

12:10 ChiRho and Its Design Principles

15:03 ChiRho’s Functionality and Use Cases

17:55 Counterfactual Worlds and Mediation Analysis

20:47 Efficient Estimation in ChiRho

24:08 Future Directions for Causal AI

50:21 Understanding the Do-Operator in Causal Inference

56:45 ChiRho’s Role in Causal Inference and Bayesian Modeling

01:01:36 Roadmap and Future Developments for ChiRho

01:05:29 Real-World Applications of Causal Probabilistic Programming

01:10:51 Challenges in Causal Inference Adoption

01:11:50 The Importance of Causal Claims in Research

01:18:11 Bayesian Approaches to Causal Inference

01:22:08 Combining Gaussian Processes with Causal Inference

01:28:27 Future Directions in Probabilistic Programming and Causal Inference

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad...

Get early access to Alex'snext live-cohort courses!

Today’s clip is from episode 140 of the podcast, with Ron Yurko.

Alex and Ron discuss the challenges of model deployment, and the complexities of modeling player contributions in team sports like soccer and football.

They emphasize the importance of understanding replacement levels, the Going Deep framework in football analytics, and the need for proper modeling of expected points.

Additionally, they share insights on teaching Bayesian modeling to students and the difficulties they face in grasping the concepts of model writing and application.

Get the full discussion here.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Get early access to Alex'snext live-cohort courses!

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

03:51 The Journey into Sports Analytics

15:20 The Evolution of Bayesian Statistics in Sports

26:01 Innovations in NFL WAR Modeling

39:23 Causal Modeling in Sports Analytics

46:29 Defining Replacement Levels in Sports

48:26 The Going Deep Framework and Big Data in Football

52:47 Modeling Expectations in Football Data

55:40 Teaching Statistical Concepts in Sports Analytics

01:01:54 The Importance of Model Building in Education

01:04:46 Statistical Thinking in Sports Analytics

01:10:55 Innovative Research in Player Movement

01:15:47 Exploring Data Needs in American Football

01:18:43 Building a Sports Analytics Portfolio

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell,...

Today’s clip is from episode 139 of the podcast, with with Max Balandat.

Alex and Max discuss the integration of BoTorch with PyTorch, exploring its applications in Bayesian optimization and Gaussian processes. They highlight the advantages of using GPyTorch for structured matrices and the flexibility it offers for research.

The discussion also covers the motivations behind building BoTorch, the importance of open-source culture at Meta, and the role of PyTorch in modern machine learning.

Get the full discussion here.

Attend Alex's tutorial at PyData Berlin: A Beginner's Guide to State Space Modeling 

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

08:51 Understanding BoTorch

12:12 Use Cases and Flexibility of BoTorch

15:02 Integration with PyTorch and GPyTorch

17:57 Practical Applications of BoTorch

20:50 Open Source Culture at Meta and BoTorch's Development

43:10 The Power of Open Source Collaboration

47:49 Scalability Challenges at Meta

51:02 Balancing Depth and Breadth in Problem Solving

55:08 Communicating Uncertainty to Stakeholders

01:00:53 Learning from Missteps in Research

01:05:06 Integrating External Contributions into BoTorch

01:08:00 The Future of Optimization with LLMs

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode,...

Today’s clip is from episode 138 of the podcast, with Mélodie Monod, François-Xavier Briol and Yingzhen Li.

During this live show at Imperial College London, Alex and his guests delve into the complexities and advancements in Bayesian deep learning, focusing on uncertainty quantification, the integration of machine learning tools, and the challenges faced in simulation-based inference.

The speakers discuss their current projects, the evolution of Bayesian models, and the need for better computational tools in the field.

Get the full discussion here.

Attend Alex's tutorial at PyData Berlin: A Beginner's Guide to State Space Modeling 

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

00:00 Introduction to Bayesian Deep Learning

03:12 Panelist Introductions and Backgrounds

10:37 Current Research and Challenges in Bayesian Deep Learning

18:04 Contrasting Approaches: Bayesian vs. Machine Learning

26:09 Tools and Techniques for Bayesian Deep Learning

31:18 Innovative Methods in Uncertainty Quantification

36:23 Generalized Bayesian Inference and Its Implications

41:38 Robust Bayesian Inference and Gaussian Processes

44:24 Software Development in Bayesian Statistics

46:51 Understanding Uncertainty in Language Models

50:03 Hallucinations in Language Models

53:48 Bayesian Neural Networks vs Traditional Neural Networks

58:00 Challenges with Likelihood Assumptions

01:01:22 Practical Applications of Uncertainty Quantification

01:04:33 Meta Decision-Making with Uncertainty

01:06:50 Exploring Bayesian Priors in Neural Networks

01:09:17 Model Complexity and Data Signal

01:12:10 Marginal Likelihood and Model Selection

01:15:03 Implementing Bayesian Methods in LLMs

01:19:21 Out-of-Distribution Detection in LLMs

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer,...

Today’s clip is from episode 137 of the podcast, with Robert Ness.

Alex and Robert discuss the intersection of causal inference and deep learning, emphasizing the importance of understanding causal concepts in statistical modeling.

The discussion also covers the evolution of probabilistic machine learning, the role of inductive biases, and the potential of large language models in causal analysis, highlighting their ability to translate natural language into formal causal queries.

Get the full conversation here.

Attend Alex's tutorial at PyData Berlin: A Beginner's Guide to State Space Modeling 

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

00:00 Introduction to Causal AI and Its Importance

16:34 The Journey to Writing Causal AI

28:05 Integrating Graphical Causality with Deep Learning

40:10 The Evolution of Probabilistic Machine Learning

44:34 Practical Applications of Causal AI with LLMs

49:48 Exploring Multimodal Models and Causality

56:15 Tools and Frameworks for Causal AI

01:03:19 Statistical Rigor in Evaluating LLMs

01:12:22 Causal Thinking in Real-World Deployments

01:19:52 Trade-offs in Generative Causal Models

01:25:14 Future of Causal Generative Modeling

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Marcus Nölke, Maggi Mackintosh, Grant...

Today’s clip is from episode 136 of the podcast, with Haavard Rue & Janet van Niekerk.

Alex, Haavard and Janet explore the world of Bayesian inference with INLA, a fast and deterministic method that revolutionizes how we handle large datasets and complex models.

Discover the power of INLA, and why it can make your models go much faster! Get the full conversation here.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

06:06 Understanding INLA: A Comparison with MCMC

08:46 Applications of INLA in Real-World Scenarios

11:58 Latent Gaussian Models and Their Importance

15:12 Impactful Applications of INLA in Health and Environment

18:09 Computational Challenges and Solutions in INLA

21:06 Stochastic Partial Differential Equations in Spatial Modeling

23:55 Future Directions and Innovations in INLA

39:51 Exploring Stochastic Differential Equations

43:02 Advancements in INLA Methodology

50:40 Getting Started with INLA

56:25 Understanding Priors in Bayesian Models

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad

Get 10% offHugo's "Building LLM Applications for Data Scientists and Software Engineers" online course!

Today’s clip is from episode 135 of the podcast, with Teemu Säilynoja.

Alex and Teemu discuss the importance of simulation-based calibration (SBC). They explore the practical implementation of SBC in probabilistic programming languages, the challenges faced in developing SBC methods, and the significance of both prior and posterior SBC in ensuring model reliability.

The discussion emphasizes the need for careful model implementation and inference algorithms to achieve accurate calibration.

Get the full conversation here.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

09:53 Understanding Simulation-Based Calibration (SBC)

15:03 Practical Applications of SBC in Bayesian Modeling

22:19 Challenges in Developing Posterior SBC

29:41 The Role of SBC in Amortized Bayesian Inference

33:47 The Importance of Visual Predictive Checking

36:50 Predictive Checking and Model Fitting

38:08 The Importance of Visual Checks

40:54 Choosing Visualization Types

49:06 Visualizations as Models

55:02 Uncertainty Visualization in Bayesian Modeling

01:00:05 Future Trends in Probabilistic Modeling

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand...

ICYMI, I'll be in London next week, for a live episode of the Learning Bayesian Statistics podcast 🍾

Come say hi on June 24 at Imperial College London! We'll be talking about uncertainty quantification — not just in theory, but in the messy, practical reality of building models that are supposed to work in the real world.

🎟️Get your tickets!

Some of the questions we’ll unpack:

🔍 Why is it so hard to model uncertainty reliably?

⚠️ How do overconfident models break things in production?

🧠 What tools and frameworks help today?

🔄 What do we need to rethink if we want robust ML over the next decade?

Joining me on stage: the brilliant Mélodie Monod, Yingzhen Li and François-Xavier Briol -- researchers doing cutting-edge work on these questions, across Bayesian methods, statistical learning, and real-world ML deployment.

A huge thank you to Oliver Ratmann for setting this up!

📍 Imperial-X, White City Campus (Room LRT 608)

🗓️ June 24, 11:30–13:00

🎙️ Doors open at 11:30 — we start at noon sharp

Come say hi, ask hard questions, and be part of the recording.

🎟️Get your tickets!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh,...

Today’s clip is from episode 134 of the podcast, with David Kohns.

Alex and David discuss the future of probabilistic programming, focusing on advancements in time series modeling, model selection, and the integration of AI in prior elicitation.

The discussion highlights the importance of setting appropriate priors, the challenges of computational workflows, and the potential of normalizing flows to enhance Bayesian inference.

Get the full discussion here.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

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Takeaways:

Chapters:

10:09 Understanding State Space Models

14:53 Predictively Consistent Priors

20:02 Dynamic Regression and AR Models

25:08 Inflation Forecasting

50:49 Understanding Time Series Data and Economic Analysis

57:04 Exploring Dynamic Regression Models

01:05:52 The Role of Priors

01:15:36 Future Trends in Probabilistic Programming

01:20:05 Innovations in Bayesian Model Selection

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki...

Today’s clip is from episode 133 of the podcast, with Sean Pinkney & Adrian Seyboldt.

The conversation delves into the concept of Zero-Sum Normal and its application in statistical modeling, particularly in hierarchical models.

Alex, Sean and Adrian discuss the implications of using zero-sum constraints, the challenges of incorporating new data points, and the importance of distinguishing between sample and population effects.

They also explore practical solutions for making predictions based on population parameters and the potential for developing tools to facilitate these processes.

Get the full discussion here.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

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Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

03:35 Sean Pinkney's Journey to Bayesian Modeling

11:21 The Zero-Sum Normal Project Explained

18:52 Technical Insights on Zero-Sum Constraints

32:04 Handling New Elements in Bayesian Models

36:19 Understanding Population Parameters and Predictions

49:11 Exploring Flexible Cholesky Parameterization

01:07:23 Closing Thoughts and Future Directions

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary...

Today’s clip is from episode 132 of the podcast, with Tom Griffiths.

Tom and Alex Andorra discuss the fundamental differences between human intelligence and artificial intelligence, emphasizing the constraints that shape human cognition, such as limited data, computational resources, and communication bandwidth.

They explore how AI systems currently learn and the potential for aligning AI with human cognitive processes.

The discussion also delves into the implications of AI in enhancing human decision-making and the importance of understanding human biases to create more effective AI systems.

Get the full discussionhere.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit ourPatreon pageto unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Check outHugo’s latest episodewith Fei-Fei Li, on How Human-Centered AI Actually Gets Built

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

00:00 Understanding Computational Cognitive Science

13:52 Bayesian Models and Human Cognition

29:50 Eliciting Implicit Prior Distributions

38:07 The Relationship Between Human and AI Intelligence

45:15 Aligning Human and Machine Preferences

50:26 Innovations in AI and Human Interaction

55:35 Resource Rationality in Decision Making

01:00:07 Language Learning in AI Models

Today’s clip is from episode 131 of the podcast, with Luke Bornn.

Luke and Alex discuss the application of generative models in sports analytics. They emphasize the importance of Bayesian modeling to account for uncertainty and contextual variations in player data.

The discussion also covers the challenges of balancing model complexity with computational efficiency, the innovative ways to hack Bayesian models for improved performance, and the significance of understanding model fitting and discretization in statistical modeling.

Get the full discussion here.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık and Suyog Chandramouli.

Takeaways:

Today’s clip is from episode 130 of the podcast, with epidemiological modeler Adam Kucharski.

This conversation explores the critical role of patient modeling during the COVID-19 pandemic, highlighting how these models informed public health decisions and the relationship between modeling and policy.

The discussion emphasizes the need for improved communication and understanding of data among the public and policymakers.

Get the full discussionhere.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık and Suyog Chandramouli.

Takeaways:

Today’s clip is from episode 129 of the podcast, with AI expert and researcher Vincent Fortuin.

This conversation delves into the intricacies of Bayesian deep learning, contrasting it with traditional deep learning and exploring its applications and challenges.

Get the full discussion at https://learnbayesstats.com/episode/129-bayesian-deep-learning-ai-for-science-vincent-fortuin

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

00:00 Introduction to Bayesian Deep Learning

06:12 Vincent's Journey into Machine Learning

12:42 Defining Bayesian Deep Learning

17:23 Current Landscape of Bayesian Libraries

22:02 Real-World Applications of Bayesian Deep Learning

24:29 When to Use Bayesian Deep Learning

29:36 Data Efficient AI and Generative Modeling

31:59 Exploring Generative AI and Meta-Learning

34:19 Understanding Bayesian Deep Learning and Prior Knowledge

39:01 Algorithms for Bayesian Deep Learning Models

43:25 Advancements in Efficient Inference Techniques

49:35 The Future of AI Models and Reliability

52:47 Advice for Aspiring Researchers in AI

56:06 Future Projects and Research Directions

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade,...

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

00:00 Introduction to Football Analytics and Matt's Journey

04:54 The Role of Bayesian Methods in Football

10:20 Challenges in Communicating Data Insights

17:03 Building Relationships with Coaches

22:09 The Structure of the Data Team at Como

26:18 Focus on Player Recruitment and Transfer Strategies

28:48 January Transfer Window Insights

30:54 Biases in Football Data Analysis

34:11 Comparative Analysis of Men's and Women's Football

36:55 Statistical Techniques in Football Analysis

42:48 The Impact of Tracking Data on Football Analysis

45:49 The Future of Data-Driven Football Strategies

47:27 Advice for Aspiring Football Analysts

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia and Michael Cao.

Takeaways:

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

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Takeaways:

Chapters:

00:00 Introduction to Will Dean and His Work

10:48 Diving into PyMC Marketing

17:10 Understanding Media Mix Modeling

25:54 Challenges in Productionizing Models

35:27 Exploring Customer Lifetime Value Models

44:10 Learning and Development in Data Science

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz,...

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

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Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire and Mike Loncaric.

Takeaways:

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

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Takeaways:

Chapters:

00:00 Introduction to Jesse Krabowski and Time Series Analysis

04:33 Jesse's Journey into Bayesian Statistics

10:51 Exploring State Space Models

18:28 Understanding State Space Models and Their Components

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Takeaways:

Chapters:

00:00 Introduction to Osvaldo Martin and Bayesian Statistics

08:12 Exploring Bayesian Additive Regression Trees (BART)

18:45 Prior Elicitation and the PreliZ Package

29:56 Teaching Bayesian Statistics and Future Directions

45:59 Exploring Prior Predictive Distributions

52:08 Interactive Modeling with PreliZ

54:06 The Evolution of ArviZ

01:01:23 Advancements in ArviZ 1.0

01:06:20 Educational Initiatives in Bayesian Statistics

01:12:33 The Future of Bayesian Methods

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin...

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

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Takeaways:

Chapters:

09:13 Hugo's Journey in Data Science and Education

14:57 The Appeal of Bayesian Statistics

19:36 Learning and Teaching in Data Science

24:53 Key Ingredients for Effective Data Science Education

28:44 Podcasting Journey and Insights

36:10 Building LLM Applications: Course Overview

42:08 Navigating the Software Development Lifecycle

48:06 Overcoming Proof of Concept Purgatory

55:35 Guidance for Aspiring Data Scientists

01:03:25 Exciting Trends in Data Science and AI

01:10:51 Balancing Multiple Roles in Data Science

01:15:23 Envisioning Accessible Data Science for All

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

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Takeaways:

Chapters:

10:11 Understanding Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA)

20:11 Application of SEM and CFA in HR Analytics

30:10 Challenges and Advantages of Bayesian Approaches in SEM and CFA

33:58 Evaluating Bayesian Models

39:50 Challenges in Model Building

44:15 Causal Relationships in SEM and CFA

49:01 Practical Applications of SEM and CFA

51:47 Influence of Philosophy on Data Science

54:51 Designing Models with Confounding in Mind

57:39 Future Trends in Causal Inference

01:00:03 Advice for Aspiring Data Scientists

01:02:48 Future Research Directions

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy,

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Takeaways:

Chapters:

00:00 Introduction to Bayesian Statistics and Epidemiology

03:35 Guest Backgrounds and Their Journey

10:04 Understanding Computational Biology vs. Epidemiology

16:11 The Role of Bayesian Statistics in Epidemiology

21:40 Recent Projects and Applications in Epidemiology

31:30...

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

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Takeaways:

Chapters:

00:00 Introduction to Bayesian Statistics and Bob Kubinec

06:01 Bob's Academic Journey and Research Focus

12:40 Measuring Corruption: Challenges and Methods

18:54 Transition from Government to Academia

26:41 The Influence of Non-Traditional Backgrounds in Statistics

34:51 Bayesian Methods in Political Science Research

42:08 Bayesian Methods in COVID Measurement

51:12 The Journey of Writing a Novel

01:00:24 The Intersection of Fiction and Academia

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell,...

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

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Takeaways:

Chapters:

00:00 Introduction to the Live Episode

02:55 Meet the Stan Core Developers

05:47 Brian Ward's Journey into Bayesian Statistics

09:10 Charles Margossian's Contributions to Stan

11:49 Recent Projects and Innovations in Stan

15:07 User-Friendly Features and Enhancements

18:11 Understanding Tuples and Their Importance

21:06 Challenges for Beginners in Stan

24:08 Pedagogical Approaches to Bayesian Statistics

30:54 Optimizing Monte Carlo Estimators

32:24 Reimagining Stan's Structure

34:21 The Promise of Automatic Reparameterization

35:49 Exploring BridgeStan

40:29 The Future of Samplers in Stan

43:45 Evaluating New Algorithms

47:01 Specific Algorithms for Unique Problems

50:00 Understanding Model Performance

54:21 The Impact of Stan on Bayesian Research

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin...

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

00:00 Introduction to Bayesian Experimental Design

07:51 Understanding Bayesian Experimental Design

19:58 Computational Challenges in Bayesian Experimental Design

28:47 Innovations in Bayesian Experimental Design

40:43 Practical Applications of Bayesian Experimental Design

52:12 Future of Bayesian Experimental Design

01:01:17 Real-World Applications and Impact

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov,...

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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

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Takeaways:

Chapters:

00:00 Introduction to Ravi and His Role at Seattle Sounders 

06:30 Building an Analytics Department

15:00 The Impact of Analytics on Player Recruitment and Performance 

28:00 Challenges and Innovations in Soccer Analytics 

42:00 Player Health, Injury Prevention, and Training 

55:00 The Evolution of Data-Driven Strategies

01:10:00 Future of Analytics in Sports

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson,

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

00:00 The Role of Nutrition and Conditioning

05:46 Analyzing Player Performance and Managing Injury Risks

12:13 Educating Athletes on Dietary Choices

18:02 Emerging Trends in Baseball Science

29:49 Hierarchical Models and Player Analysis

36:03 Challenges of Working with Limited Data

39:49 Effective Communication of Statistical Concepts

47:59 Future Trends: Biomechanical Data Analysis and Computer Vision Algorithms

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde,...

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

00:00 Introduction to Bayesian Statistics

07:32 Advantages of Bayesian Methods

16:22 Incorporating Priors in Models

23:26 Modeling Causal Relationships

30:03 Introduction to PyMC, Stan, and Bambi

34:30 Choosing the Right Bayesian Framework

39:20 Getting Started with Bayesian Statistics

44:39 Understanding Bayesian Statistics and PyMC

49:01 Leveraging PyTensor for Improved Performance and Scalability

01:02:37 Exploring Post-Modeling Workflows with ArviZ

01:08:30 The Power of Gaussian Processes in Bayesian Modeling

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna,...

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

05:36 Tomi's Work and Teaching

10:28 Teaching Complex Statistical Concepts with Practical Exercises

23:17 Making Bayesian Modeling Accessible in Python

38:46 Advanced Regression with Bambi

41:14 The Power of Linear Regression

42:45 Exploring Advanced Regression Techniques

44:11 Regression Models and Dot Products

45:37 Advanced Concepts in Regression

46:36 Diagnosing and Handling Overdispersion

47:35 Parameter Identifiability and Overparameterization

50:29 Visualizations and Course Highlights

51:30 Exploring Niche and Advanced Concepts

56:56 The Power of Zero-Sum Normal

59:59 The Value of Exercises and Community

01:01:56 Optimizing Computation with Sparse Matrices

01:13:37 Avoiding MCMC and Exploring Alternatives

01:18:27 Making Connections Between Different Models

Thank you to my Patrons for making this episode...

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

00:00 Bayesian Statistics in Sports Analytics

18:29 Applying Bayesian Stats in Analyzing Player Performance and Injury Risk

36:21 Challenges in Communicating Bayesian Concepts to Non-Statistical Decision-Makers

41:04 Understanding Model Behavior and Validation through Simulations

43:09 Applying Bayesian Methods in Sports Analytics

48:03 Clarifying Questions and Utilizing Frameworks

53:41 Effective Communication of Statistical Concepts

57:50 Integrating Domain Expertise with Statistical Models

01:13:43 The Importance of Good Data

01:18:11 The Future of Sports Analytics

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew...

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

Chapters:

00:00 Introduction to Large-Scale Machine Learning

11:26 Scalable and Flexible Bayesian Inference with Posteriors

25:56 The Role of Temperature in Bayesian Models

32:30 Stochastic Gradient MCMC for Large Datasets

36:12 Introducing Posteriors: Bayesian Inference in Machine Learning

41:22 Uncertainty Quantification and Improved Predictions

52:05 Supporting New Algorithms and Arbitrary Likelihoods

59:16 Thermodynamic Computing

01:06:22 Decoupling Model Specification, Data Generation, and Inference

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways

Chapters

00:00 The Power and Importance of Priors

09:29 Updating Beliefs and Choosing Reasonable Priors

16:08 Assessing Robustness with Prior Sensitivity Analysis

34:53 Aligning Bayesian Methods with Researchers' Thinking

37:10 Detecting Overfitting in SEM

43:48 Evaluating Model Fit with Posterior Predictive Checks

47:44 Teaching Bayesian Methods

54:07 Future Developments in Bayesian Statistics

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi...

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out hisawesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways

Chapters

00:00 Introduction and Overview

09:27 The Power of Bayesian Analysis in Sports Modeling

16:28 The Revolution of Massive Data Sets in Sports Analytics

31:03 The Impact of Budget in Sports Analytics

39:35 Introduction to Sports Analytics

52:22 Plus-Minus Models in American Football

01:04:11 The Future of Sports Analytics

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi...

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In this episode, Marvin Schmitt introduces the concept of amortized Bayesian inference, where the upfront training phase of a neural network is followed by fast posterior inference.

Marvin will guide us through this new concept, discussing his work in probabilistic machine learning and uncertainty quantification, using Bayesian inference with deep neural networks. 

He also introduces BayesFlow, a Python library for amortized Bayesian workflows, and discusses its use cases in various fields, while also touching on the concept of deep fusion and its relation to multimodal simulation-based inference.

A PhD student in computer science at the University of Stuttgart, Marvin is supervised by two LBS guests you surely know — Paul Bürkner and Aki Vehtari. Marvin’s research combines deep learning and statistics, to make Bayesian inference fast and trustworthy. 

In his free time, Marvin enjoys board games and is a passionate guitar player.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work athttps://bababrinkman.com/!

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary and Blake Walters.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Takeaways:

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If there is one guest I don’t need to introduce, it’s mister Andrew Gelman. So… I won’t! I will refer you back to his two previous appearances on the show though, because learning from Andrew is always a pleasure. So go ahead and listen to episodes 20 and 27.

In this episode, Andrew and I discuss his new book, Active Statistics, which focuses on teaching and learning statistics through active student participation. Like this episode, the book is divided into three parts: 1) The ideas of statistics, regression, and causal inference; 2) The value of storytelling to make statistical concepts more relatable and interesting; 3) The importance of teaching statistics in an active learning environment, where students are engaged in problem-solving and discussion.

And Andrew is so active and knowledgeable that we of course touched on a variety of other topics — but for that, you’ll have to listen ;)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work athttps://bababrinkman.com/!

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary and Blake Walters.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Takeaways:

- Active learning is essential for teaching and learning statistics.

- Storytelling can make...

Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget in touch!

In this episode, Andy Aschwanden and Doug Brinkerhoff tell us about their work in glaciology and the application of Bayesian statistics in studying glaciers. They discuss the use of computer models and data analysis in understanding glacier behavior and predicting sea level rise, and a lot of other fascinating topics.

Andy grew up in the Swiss Alps, and studied Earth Sciences, with a focus on atmospheric and climate science and glaciology. After his PhD, Andy moved to Fairbanks, Alaska, and became involved with the Parallel Ice Sheet Model, the first open-source and openly-developed ice sheet model.

His first PhD student was no other than… Doug Brinkerhoff! Doug did an MS in computer science at the University of Montana, focusing on numerical methods for ice sheet modeling, and then moved to Fairbanks to complete his PhD. While in Fairbanks, he became an ardent Bayesian after “seeing that uncertainty needs to be embraced rather than ignored”. Doug has since moved back to Montana, becoming faculty in the University of Montana’s computer science department.

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work athttps://bababrinkman.com/!

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero and Will Geary.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)