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Learning Bayesian Statistics

Learning Bayesian Statistics

Von: 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 Wissenschaft
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  • BITESIZE | Practical Applications of Causal AI with LLMs, with Robert Ness

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

    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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    25 Min.
  • #137 Causal AI & Generative Models, with Robert Ness

    #137 Causal AI & Generative Models, with Robert Ness
    Jul 23 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:

    • Causal assumptions are crucial for statistical modeling.
    • Deep learning can be integrated with causal models.
    • Statistical rigor is essential in evaluating LLMs.
    • Causal representation learning is a growing field.
    • Inductive biases in AI should match key mechanisms.
    • Causal AI can improve decision-making processes.
    • The future of AI lies in understanding causal relationships.

    Chapters:

    00:00 Introduction to Causal AI and Its Importance

    16:34 The Journey to Writing Causal AI

    28:05 Integrating Graphical Causality with Deep Learning

    40:10 The Evolution of Probabilistic Machine Learning

    44:34 Practical Applications of Causal AI with LLMs

    49:48 Exploring Multimodal Models and Causality

    56:15 Tools and Frameworks for Causal AI

    01:03:19 Statistical Rigor in Evaluating LLMs

    01:12:22 Causal Thinking in Real-World Deployments

    01:19:52 Trade-offs in Generative Causal Models

    01:25:14 Future of Causal Generative Modeling

    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, Gabriel Stechschulte, Arkady, Kurt TeKolste, Marcus Nölke, Maggi Mackintosh, Grant...
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    1 Std. und 38 Min.
  • BITESIZE | How to Make Your Models Faster, with Haavard Rue & Janet van Niekerk

    BITESIZE | How to Make Your Models Faster, with Haavard Rue & Janet van Niekerk
    Jul 16 2025

    Today’s clip is from episode 136 of the podcast, with Haavard Rue & Janet van Niekerk.

    Alex, Haavard and Janet explore the world of Bayesian inference with INLA, a fast and deterministic method that revolutionizes how we handle large datasets and complex models.

    Discover the power of INLA, and why it can make your models go much faster! Get the full conversation 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.
    Mehr anzeigen Weniger anzeigen
    18 Min.
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