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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
  • BITESIZE | How Probability Becomes Causality?

    BITESIZE | How Probability Becomes Causality?
    Sep 24 2025

    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.
    Show More Show Less
    22 mins
  • #141 AI Assisted Causal Inference, with Sam Witty

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

    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...
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    1 hr and 38 mins
  • BITESIZE | How to Think Causally About Your Models?

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

    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.
    Show More Show Less
    24 mins
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