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    Technology

    Learning Bayesian Statistics

    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. By day, I’m a Senior data scientist. By night, I don’t (yet) fight crime, but I’m an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. I also love 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 unlock exclusive Bayesian swag on Patreon!

    Advertise

    Copyright: © Copyright Alexandre Andorra

    • Apple Podcasts
    • Google Play
    • Spotify

    Latest Episodes:
    #142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte Oct 02, 2025
    Show notes

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

    • Get early access to Alex's next live-cohort courses!
    • Intro to Bayes Course (first 2 lessons free)
    • Advanced Regression 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!

    Visit our Patreon page 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...


    BITESIZE | How Probability Becomes Causality? Sep 24, 2025
    Show notes

    Get early access to Alex's next 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.

    • Intro to Bayes Course (first 2 lessons free)
    • Advanced Regression 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!

    Visit our Patreon page to unlock exclusive Bayesian swag ;)

    Transcript

    This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.


    #141 AI Assisted Causal Inference, with Sam Witty Sep 18, 2025
    Show notes

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

    • Get early access to Alex's next live-cohort courses!
    • Enroll in the Causal AI workshop, 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!

    Visit our Patreon page 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...


    BITESIZE | How to Think Causally About Your Models? Sep 10, 2025
    Show notes

    Get early access to Alex's next 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.

    • Intro to Bayes Course (first 2 lessons free)
    • Advanced Regression 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!

    Visit our Patreon page to unlock exclusive Bayesian swag ;)

    Transcript

    This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.


    #140 NFL Analytics & Teaching Bayesian Stats, with Ron Yurko Sep 03, 2025
    Show notes

    Get early access to Alex's next live-cohort courses!

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

    • Intro to Bayes Course (first 2 lessons free)
    • Advanced Regression 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!

    Visit our Patreon page 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,...


    BITESIZE | Is Bayesian Optimization the Answer? Aug 27, 2025
    Show notes

    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

    • Intro to Bayes Course (first 2 lessons free)
    • Advanced Regression 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!

    Visit our Patreon page to unlock exclusive Bayesian swag ;)

    Transcript

    This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.


    #139 Efficient Bayesian Optimization in PyTorch, with Max Balandat Aug 20, 2025
    Show notes

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

    • Intro to Bayes Course (first 2 lessons free)
    • Advanced Regression 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!

    Visit our Patreon page to unlock exclusive Bayesian swag ;)

    Takeaways:

    • BoTorch is designed for researchers who want flexibility in Bayesian optimization.
    • The integration of BoTorch with PyTorch allows for differentiable programming.
    • Scalability at Meta involves careful software engineering practices and testing.
    • Open-source contributions enhance the development and community engagement of BoTorch.
    • LLMs can help incorporate human knowledge into optimization processes.
    • Max emphasizes the importance of clear communication of uncertainty to stakeholders.
    • The role of a researcher in industry is often more application-focused than in academia.
    • Max's team at Meta works on adaptive experimentation and Bayesian optimization.

    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,...


    BITESIZE | What's Missing in Bayesian Deep Learning? Aug 13, 2025
    Show notes

    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

    • Intro to Bayes Course (first 2 lessons free)
    • Advanced Regression 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!

    Visit our Patreon page to unlock exclusive Bayesian swag ;)

    Transcript

    This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.


    #138 Quantifying Uncertainty in Bayesian Deep Learning, Live from Imperial College London Aug 06, 2025
    Show notes

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

    • Intro to Bayes Course (first 2 lessons free)
    • Advanced Regression 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!

    Visit our Patreon page to unlock exclusive Bayesian swag ;)

    Takeaways:

    • Bayesian deep learning is a growing field with many challenges.
    • Current research focuses on applying Bayesian methods to neural networks.
    • Diffusion methods are emerging as a new approach for uncertainty quantification.
    • The integration of machine learning tools into Bayesian models is a key area of research.
    • The complexity of Bayesian neural networks poses significant computational challenges.
    • Future research will focus on improving methods for uncertainty quantification. Generalized Bayesian inference offers a more robust approach to uncertainty.
    • Uncertainty quantification is crucial in fields like medicine and epidemiology.
    • Detecting out-of-distribution examples is essential for model reliability.
    • Exploration-exploitation trade-off is vital in reinforcement learning.
    • Marginal likelihood can be misleading for model selection.
    • The integration of Bayesian methods in LLMs presents unique challenges.

    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,...


    BITESIZE | Practical Applications of Causal AI with LLMs, with Robert Ness Jul 30, 2025
    Show notes

    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

    • Intro to Bayes Course (first 2 lessons free)
    • Advanced Regression 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!

    Visit our Patreon page to unlock exclusive Bayesian swag ;)

    Transcript

    This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.


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