Couverture de Learning Bayesian Statistics

Learning Bayesian Statistics

Learning Bayesian Statistics

De : Alexandre Andorra
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 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
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    Épisodes
    • #147 Fast Approximate Inference without Convergence Worries, with Martin Ingram

      #147 Fast Approximate Inference without Convergence Worries, with Martin Ingram
      Dec 12 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:

      • DADVI is a new approach to variational inference that aims to improve speed and accuracy.
      • DADVI allows for faster Bayesian inference without sacrificing model flexibility.
      • Linear response can help recover covariance estimates from mean estimates.
      • DADVI performs well in mixed models and hierarchical structures.
      • Normalizing flows present an interesting avenue for enhancing variational inference.
      • DADVI can handle large datasets effectively, improving predictive performance.
      • Future enhancements for DADVI may include GPU support and linear response integration.

      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...
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      1 h et 10 min
    • BITESIZE | Why Bayesian Stats Matter When the Physics Gets Extreme

      BITESIZE | Why Bayesian Stats Matter When the Physics Gets Extreme
      Dec 5 2025

      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.

      • 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.
      Afficher plus Afficher moins
      19 min
    • #146 Lasers, Planets, and Bayesian Inference, with Ethan Smith

      #146 Lasers, Planets, and Bayesian Inference, with Ethan Smith
      Nov 27 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:

      • Ethan's research involves using lasers to compress matter to extreme conditions to study astrophysical phenomena.
      • Bayesian inference is a key tool in analyzing complex data from high energy density experiments.
      • The future of high energy density physics lies in developing new diagnostic technologies and increasing experimental scale.
      • High energy density physics can provide insights into planetary science and astrophysics.
      • Emerging technologies in diagnostics are set to revolutionize the field.
      • Ethan's dream project involves exploring picno nuclear fusion.

      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,
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      1 h et 35 min
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