Learning Bayesian Statistics

A podcast by Alexandre Andorra

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132 Episoade

  1. Reactive Message Passing in Bayesian Inference

    Publicat: 28.02.2024
  2. #100 Reactive Message Passing & Automated Inference in Julia, with Dmitry Bagaev

    Publicat: 21.02.2024
  3. The biggest misconceptions about Bayes & Quantum Physics

    Publicat: 16.02.2024
  4. Why would you use Bayesian Statistics?

    Publicat: 14.02.2024
  5. #99 Exploring Quantum Physics with Bayesian Stats, with Chris Ferrie

    Publicat: 09.02.2024
  6. How do sampling algorithms scale?

    Publicat: 05.02.2024
  7. Why choose new algorithms instead of HMC?

    Publicat: 04.02.2024
  8. #98 Fusing Statistical Physics, Machine Learning & Adaptive MCMC, with Marylou Gabrié

    Publicat: 24.01.2024
  9. Why Even Care About Science & Rationality

    Publicat: 20.01.2024
  10. How To Get Into Causal Inference

    Publicat: 17.01.2024
  11. #97 Probably Overthinking Statistical Paradoxes, with Allen Downey

    Publicat: 09.01.2024
  12. How to Choose & Use Priors, with Daniel Lee

    Publicat: 20.12.2023
  13. Becoming a Good Bayesian & Choosing Mentors, with Daniel Lee

    Publicat: 13.12.2023
  14. #96 Pharma Models, Sports Analytics & Stan News, with Daniel Lee

    Publicat: 28.11.2023
  15. #95 Unraveling Cosmic Mysteries, with Valerie Domcke

    Publicat: 15.11.2023
  16. #94 Psychometrics Models & Choosing Priors, with Jonathan Templin

    Publicat: 24.10.2023
  17. #93 A CERN Odyssey, with Kevin Greif

    Publicat: 18.10.2023
  18. #92 How to Make Decision Under Uncertainty, with Gerd Gigerenzer

    Publicat: 04.10.2023
  19. #91, Exploring European Football Analytics, with Max Göbel

    Publicat: 20.09.2023
  20. #90, Demystifying MCMC & Variational Inference, with Charles Margossian

    Publicat: 06.09.2023

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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!

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