145 Episodes

  1. #98 Fusing Statistical Physics, Machine Learning & Adaptive MCMC, with Marylou Gabrié

    Published: 1/24/2024

  2. Why Even Care About Science & Rationality

    Published: 1/20/2024

  3. How To Get Into Causal Inference

    Published: 1/17/2024

  4. #97 Probably Overthinking Statistical Paradoxes, with Allen Downey

    Published: 1/9/2024

  5. How to Choose & Use Priors, with Daniel Lee

    Published: 12/20/2023

  6. Becoming a Good Bayesian & Choosing Mentors, with Daniel Lee

    Published: 12/13/2023

  7. #96 Pharma Models, Sports Analytics & Stan News, with Daniel Lee

    Published: 11/28/2023

  8. #95 Unraveling Cosmic Mysteries, with Valerie Domcke

    Published: 11/15/2023

  9. #94 Psychometrics Models & Choosing Priors, with Jonathan Templin

    Published: 10/24/2023

  10. #93 A CERN Odyssey, with Kevin Greif

    Published: 10/18/2023

  11. #92 How to Make Decision Under Uncertainty, with Gerd Gigerenzer

    Published: 10/4/2023

  12. #91, Exploring European Football Analytics, with Max Göbel

    Published: 9/20/2023

  13. #90, Demystifying MCMC & Variational Inference, with Charles Margossian

    Published: 9/6/2023

  14. #89 Unlocking the Science of Exercise, Nutrition & Weight Management, with Eric Trexler

    Published: 8/23/2023

  15. #88 Bridging Computation & Inference in Artificial Intelligent Systems, with Philipp Hennig

    Published: 8/10/2023

  16. #87 Unlocking the Power of Bayesian Causal Inference, with Ben Vincent

    Published: 7/30/2023

  17. #86 Exploring Research Synchronous Languages & Hybrid Systems, with Guillaume Baudart

    Published: 7/14/2023

  18. #85 A Brief History of Sports Analytics, with Jim Albert

    Published: 6/27/2023

  19. #84 Causality in Neuroscience & Psychology, with Konrad Kording

    Published: 6/13/2023

  20. #83 Multilevel Regression, Post-Stratification & Electoral Dynamics, with Tarmo Jüristo

    Published: 5/25/2023

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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 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 PyMC and ArviZ. I also love 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 unlock exclusive Bayesian swag on Patreon!