 * Sign up for Alex's first live cohort [https://athlyticz.com/cohorts/alex-andorra/hierarchical], about Hierarchical Model building!
 * Get 25% off [https://bit.ly/lbs] "Building AI Applications for Data Scientists and Software Engineers"


Proudly sponsored by PyMC Labs [https://www.pymc-labs.io/], the Bayesian Consultancy. Book a call [https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf], or get in touch [alex.andorra@pymc-labs.io]!

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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Takeaways:

 * Why GPs still matter: Gaussian Processes remain a go-to for function estimation, active learning, and experimental design – especially when calibrated uncertainty is non-negotiable.
 * Scaling GP inference: Variational methods with inducing points (as in GPflow) make GPs practical on larger datasets without throwing away principled Bayes.
 * MCMC in practice: Clever parameterizations and gradient-based samplers tighten mixing and efficiency; use MCMC when you need gold-standard posteriors.
 * Bayesian deep learning, pragmatically: Stochastic-gradient training and approximate posteriors bring Bayesian ideas to neural networks at scale.
 * Uncertainty that ships: Monte Carlo dropout and related tricks provide fast, usable uncertainty – even if they’re approximations.
 * Model complexity ≠ model quality: Understanding capacity, priors, and inductive bias is key to getting trustworthy predictions.
 * Deep Gaussian Processes: Layered GPs offer flexibility for complex functions, with clear trade-offs in interpretability and compute.
 * Generative models through a Bayesian lens: GANs and friends benefit from explicit priors and uncertainty – useful for safety and downstream decisions.
 * Tooling that matters: Frameworks like GPflow lower the friction from idea to implementation, encouraging reproducible, well-tested modeling.
 * Where we’re headed: The future of ML is uncertainty-aware by default – integrating UQ tightly into optimization, design, and deployment.


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

#144 Why is Bayesian Deep Learning so Powerful, with Maurizio Filippone

 * Sign up for Alex's first live cohort, about Hierarchical Model [https://athlyticz.com/cohorts/alex-andorra/hierarchical] building
 * Soccer Factor Model Dashboard [https://soccerfactormodel.com/]


Today’s clip is from episode 143 [https://learnbayesstats.com/episode/143-transforming-nutrition-science-bayesian-methods-christoph-bamberg] 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 [https://learnbayesstats.com/episode/143-transforming-nutrition-science-bayesian-methods-christoph-bamberg].

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


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch [http://twitter.com/LearnBayesStats] if you're willing to correct them.

BITESIZE | Are Bayesian Models the Missing Ingredient in Nutrition Research?

 * Sign up for Alex's first live cohort [https://athlyticz.com/cohorts/alex-andorra/hierarchical], about Hierarchical Model building!


Proudly sponsored by PyMC Labs [https://www.pymc-labs.io/], the Bayesian Consultancy. Book a call [https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf], or get in touch [alex.andorra@pymc-labs.io]!

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


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Takeaways:

 * Bayesian mindset in psychology: Why priors, model checking, and full uncertainty reporting make findings more honest and useful.
 * Intermittent fasting & cognition: A Bayesian meta-analysis suggests effects are context- and age-dependent – and often small but meaningful.
 * Framing matters: The way we frame dietary advice (focus, flexibility, timing) can shape adherence and perceived cognitive benefits.
 * From cravings to choices: Appetite, craving, stress, and mood interact to influence eating and cognitive performance throughout the day.
 * Define before you measure: Clear definitions (and DAGs to encode assumptions) reduce ambiguity and guide better study design.
 * DAGs for causal thinking: Directed acyclic graphs help separate hypotheses from data pipelines and make causal claims auditable.
 * Small effects, big implications: Well-estimated “small” effects can scale to public-health relevance when decisions repeat daily.
 * Teaching by modeling: Helping students write models (not just run them) builds statistical thinking and scientific literacy.
 * Bridging lab and life: Balancing careful experiments with real-world measurement is key to actionable health-psychology insights.
 * Trust through transparency: Openly communicating assumptions, uncertainty, and limitations strengthens scientific credibility.


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 mak

#143 Transforming Nutrition Science with Bayesian Methods, with Christoph Bamberg

 * Soccer Factor Model Dashboard [https://soccerfactormodel.com/]
 * Unveiling True Talent: The Soccer Factor Model for Skill Evaluation [https://arxiv.org/abs/2412.05911]
 * LBS #91, Exploring European Football Analytics, with Max Göbel [https://learnbayesstats.com/episode/91-exploring-european-football-analytics-max-gobel]


Get early access to Alex's next live-cohort courses [https://forms.gle/YAT5wZj9NbFyKykB8]!

Today’s clip is from episode 142 [https://learnbayesstats.com/episode/142-bayesian-trees-deep-learning-optimization-big-data-gabriel-stechschulte] 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 [https://learnbayesstats.com/episode/142-bayesian-trees-deep-learning-optimization-big-data-gabriel-stechschulte].

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


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch [http://twitter.com/LearnBayesStats] if you're willing to correct them.

BITESIZE | How Bayesian Additive Regression Trees Work in Practice

Proudly sponsored by PyMC Labs [https://www.pymc-labs.io/], the Bayesian Consultancy. Book a call [https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf], or get in touch [alex.andorra@pymc-labs.io]!

 * Get early access to Alex's next live-cohort courses [https://forms.gle/YAT5wZj9NbFyKykB8]!
 * Intro to Bayes Course [https://topmate.io/alex_andorra/503302] (first 2 lessons free)
 * Advanced Regression Course [https://topmate.io/alex_andorra/1011122] (first 2 lessons free)


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Takeaways:

 * BART as a core tool: Gabriel explains how Bayesian Additive Regression Trees provide robust uncertainty quantification and serve as a reliable baseline model in many domains.
 * Rust for performance: His Rust re-implementation of BART dramatically improves speed and scalability, making it feasible for larger datasets and real-world IoT applications.
 * Strengths and trade-offs: BART avoids overfitting and handles missing data gracefully, though it is slower than other tree-based approaches.
 * Big data meets Bayes: Gabriel shares strategies for applying Bayesian methods with big data, including when variational inference helps balance scale with rigor.
 * Optimization and decision-making: He highlights how BART models can be embedded into optimization frameworks, opening doors for sequential decision-making.
 * Open source matters: Gabriel emphasizes the importance of communities like PyMC and Bambi, encouraging newcomers to start with small contributions.


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 Cost

#142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte

Get early access to Alex's next live-cohort courses [https://forms.gle/YAT5wZj9NbFyKykB8]!

Today’s clip is from episode 141 [https://learnbayesstats.com/episode/141-ai-assisted-causal-inference-sam-witty] 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 [https://learnbayesstats.com/episode/141-ai-assisted-causal-inference-sam-witty].

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


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch [http://twitter.com/LearnBayesStats] if you're willing to correct them.

BITESIZE | How Probability Becomes Causality?

Proudly sponsored by PyMC Labs [https://www.pymc-labs.io/], the Bayesian Consultancy. Book a call [https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf], or get in touch [alex.andorra@pymc-labs.io]!

 * Get early access to Alex's next live-cohort courses [https://forms.gle/YAT5wZj9NbFyKykB8]!
 * Enroll in the Causal AI workshop [https://www.altdeep.ai/p/causalml], to learn live with Alex (15% off if you're a Patron of the show)


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Takeaways:

 * Causal inference is crucial for understanding the impact of interventions in various fields.
 * ChiRho is a causal probabilistic programming language that bridges mechanistic and data-driven models.
 * ChiRho allows for easy manipulation of causal models and counterfactual reasoning.
 * The design of ChiRho emphasizes modularity and extensibility for diverse applications.
 * Causal inference requires careful consideration of assumptions and model structures.
 * Real-world applications of causal inference can lead to significant insights in science and engineering.
 * Collaboration and communication are key in translating causal questions into actionable models.
 * The future of causal inference lies in integrating probabilistic programming with scientific discovery.


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 Scherrer, Z

#141 AI Assisted Causal Inference, with Sam Witty

Today’s clip is from episode 140 [https://learnbayesstats.com/episode/140-nfl-analytics-teaching-bayesian-stats-ron-yurko] 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 [https://learnbayesstats.com/episode/140-nfl-analytics-teaching-bayesian-stats-ron-yurko].

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


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch [http://twitter.com/LearnBayesStats] if you're willing to correct them.

BITESIZE | How to Think Causally About Your Models?

Proudly sponsored by PyMC Labs [https://www.pymc-labs.io/], the Bayesian Consultancy. Book a call [https://calendar.google.com/calendar/appointments/schedules/AcZssZ1nOI_SElJzSiQ2sXBDiaW9w98ErjnHVzmHcSilYNWeXxJgV870NGuWZUGo3W-8-gDG8jIXQhBf], or get in touch [alex.andorra@pymc-labs.io]!

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


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Takeaways:

 * Teaching students to write out their own models is crucial.
 * Developing a sports analytics portfolio is essential for aspiring analysts.
 * Modeling expectations in sports analytics can be misleading.
 * Tracking data can significantly improve player performance models.
 * Ron encourages students to engage in active learning through projects.
 * The importance of understanding the dependency structure in data is vital.
 * Ron aims to integrate more diverse sports analytics topics into his teaching.


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, 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, Maso

#140 NFL Analytics & Teaching Bayesian Stats, with Ron Yurko

Today’s clip is from episode 139 [https://learnbayesstats.com/episode/139-efficient-bayesian-optimization-pytorch-max-balandat] 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 [https://learnbayesstats.com/episode/139-efficient-bayesian-optimization-pytorch-max-balandat].

Attend Alex's tutorial at PyData Berlin: A Beginner's Guide to State Space Modeling  [https://cfp.pydata.org/berlin2025/talk/GRZ3RG/]

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


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

Visit our Patreon page [https://www.patreon.com/learnbayesstats] to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch [http://twitter.com/LearnBayesStats] if you're willing to correct them.

BITESIZE | Is Bayesian Optimization the Answer?

Are you a researcher or data scientist / analyst / ninja? Do you want to learn
Bayesian inference, stay up to date or simply want to understand what Bayesian
inference is? Then this podcast is for you! You'll hear from researchers and
practitioners of all fields about how they use Bayesian statistics, and how in
turn YOU can apply these methods in your modeling workflow. When I started
learning Bayesian methods, I really wished there were a podcast out there that
could introduce me to the methods, the projects and the people who make all that
possible. So I created "Learning Bayesian Statistics", where you'll get to hear
how Bayesian statistics are used to detect black matter in outer space, forecast
elections or understand how diseases spread and can ultimately be stopped. But
this show is not only about successes -- it's also about failures, because
that's how we learn best. So you'll often hear the guests talking about what
*didn't* work in their projects, why, and how they overcame these challenges.
Because, in the end, we're all lifelong learners! My name is Alex Andorra by the
way, and I live in Paris. By day, I'm a data scientist and modeler at the
https://www.pymc-labs.io/ (PyMC Labs) consultancy. By night, I don't (yet) fight
crime, but I'm an open-source enthusiast and core contributor to the python
packages https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/
(ArviZ). I also love https://www.pollsposition.com/ (election forecasting) and,
most importantly, Nutella. But I don't like talking about it – I prefer eating
it. So, whether you want to learn Bayesian statistics or hear about the latest
libraries, books and applications, this podcast is for you -- just subscribe!
You can also support the show and https://www.patreon.com/learnbayesstats
(unlock exclusive Bayesian swag on Patreon)! This podcast uses the following
third-party services for analysis: Podcorn - https://podcorn.com/privacy

Alexandre ANDORRA

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