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Learning Bayesian Statistics

Learning Bayesian Statistics

By: Alexandre Andorra
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About this listen

 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 Estonia. 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)!Copyright Alexandre Andorra Science
Episodes
  • #134 Bayesian Econometrics, State Space Models & Dynamic Regression, with David Kohns

    #134 Bayesian Econometrics, State Space Models & Dynamic Regression, with David Kohns
    Jun 10 2025

    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:

    • Setting appropriate priors is crucial to avoid overfitting in models.
    • R-squared can be used effectively in Bayesian frameworks for model evaluation.
    • Dynamic regression can incorporate time-varying coefficients to capture changing relationships.
    • Predictively consistent priors enhance model interpretability and performance.
    • Identifiability is a challenge in time series models.
    • State space models provide structure compared to Gaussian processes.
    • Priors influence the model's ability to explain variance.
    • Starting with simple models can reveal interesting dynamics.
    • Understanding the relationship between states and variance is key.
    • State-space models allow for dynamic analysis of time series data.
    • AI can enhance the process of prior elicitation in statistical models.

    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...
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    1 hr and 41 mins
  • BITESIZE | Why Your Models Might Be Wrong & How to Fix it, with Sean Pinkney & Adrian Seyboldt

    BITESIZE | Why Your Models Might Be Wrong & How to Fix it, with Sean Pinkney & Adrian Seyboldt
    Jun 4 2025

    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.

    • 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.
    Show More Show Less
    17 mins
  • #133 Making Models More Efficient & Flexible, with Sean Pinkney & Adrian Seyboldt

    #133 Making Models More Efficient & Flexible, with Sean Pinkney & Adrian Seyboldt
    May 28 2025

    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:

    • Zero Sum constraints allow for better sampling and estimation in hierarchical models.
    • Understanding the difference between population and sample means is crucial.
    • A library for zero-sum normal effects would be beneficial.
    • Practical solutions can yield decent predictions even with limitations.
    • Cholesky parameterization can be adapted for positive correlation matrices.
    • Understanding the geometry of sampling spaces is crucial.
    • The relationship between eigenvalues and sampling is complex.
    • Collaboration and sharing knowledge enhance research outcomes.
    • Innovative approaches can simplify complex statistical problems.

    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...
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    1 hr and 12 mins
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I found the podcast when on a mission to seek any and all Bayesian information. Many fell by the wayside, but Learning Bayesian Statistics is a lovely podcast that pours a comfy chat around the real modern use of Probability.
Thanks for such interesting interviews.

Enjoyable upbeat statistics chat

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