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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:
    Bitesize | How To Model Risk Aversion In Pricing? Mar 04, 2026
    Show notes

    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/ !


    #152 A Bayesian decision theory workflow, with Daniel Saunders Feb 26, 2026
    Show notes

    • 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 my Patrons for making this episode possible!

    Links from the show:

    • Come meet Alex at the Field of Play Conference in Manchester, UK, March 27, 2026! https://www.fieldofplay.co.uk/
    • A Bayesian decision theory workflow
    • Daniel's website, LinkedIn and GitHub
    • LBS #124 State Space Models & Structural Time Series, with Jesse Grabowski
    • LBS #123 BART & The Future of Bayesian Tools, with Osvaldo Martin
    • LBS #74 Optimizing NUTS and Developing the ZeroSumNormal Distribution, with Adrian Seyboldt
    • LBS #76 The Past, Present & Future of Stan, with Bob Carpenter

    BITESIZE | How Do Diffusion Models Work? Feb 19, 2026
    Show notes

    Today's clip is from Episode 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/ !


    #151 Diffusion Models in Python, a Live Demo with Jonas Arruda Feb 12, 2026
    Show notes

    • 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 my Patrons for 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


    #150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik Jan 28, 2026
    Show notes

    • 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 to my Patrons for 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


    BITESIZE | Building Resilience in Modern Tech Careers Jan 21, 2026
    Show notes

    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/ !


    #149 The Future of Work in Tech, with Alana Karen Jan 14, 2026
    Show notes

    • 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:

    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 to my Patrons for making this episode possible!

    Links from the show:

    • Alana's latest book (Use code BAYESIAN for 10% off + a free interview preparation download PDF)
    • Alana’s Substack
    • Alana on Linkedin
    • Alana on Instagram
    • The Obstacle Is the Way – The Timeless Art of Turning Trials into Triumph
    • Courage Is Calling – Fortune Favours the Brave

    BITESIZE | The Trial Design That Learns in Real Time Jan 07, 2026
    Show notes

    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/ !


    #148 Adaptive Trials, Bayesian Thinking, and Learning from Data, with Scott Berry Dec 30, 2025
    Show notes

    • 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:

    • Berry Consultants
    • Scott's podcast
    • LBS #45 Biostats & Clinical Trial Design, with Frank Harrell

    BITESIZE | Making Variational Inference Reliable: From ADVI to DADVI Dec 17, 2025
    Show notes

    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.

    • 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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