Learning Bayesian Statistics

By Alexandre ANDORRA

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 Paris. 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)! This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy

  1. 1.
    #36 Bayesian Non-Parametrics & Developing Turing.jl, with Martin Trapp
    1:09:38
  2. 2.
    #35 The Past, Present & Future of BRMS, with Paul Bürkner
    1:07:02
  3. 3.
    #34 Multilevel Regression, Post-stratification & Missing Data, with Lauren Kennedy
    1:12:49
  4. 4.
    #33 Bayesian Structural Time Series, with Ben Zweig
    57:57
  5. 5.
    #32 Getting involved into Bayesian Stats & Open-Source Development, with Peadar Coyle
    53:12
  6. 6.
    #31 Bayesian Cognitive Modeling & Decision-Making, with Michael Lee
    1:09:18
  7. 7.
    #30 Symbolic Computation & Dynamic Linear Models, with Brandon Willard
    1:00:16
  8. 8.
    #29 Model Assessment, Non-Parametric Models, And Much More, with Aki Vehtari
    1:05:04
  1. 9.
    #28 Game Theory, Industrial Organization & Policy Design, with Shosh Vasserman
    1:03:56
  2. 10.
    #27 Modeling the US Presidential Elections, with Andrew Gelman & Merlin Heidemanns
    1:00:52
  3. 11.
    #26 What you'll learn & who you'll meet at the PyMC Conference, with Ravin Kumar & Quan Nguyen
    46:27
  4. 12.
    #25 Bayesian Stats in Football Analytics, with Kevin Minkus
    55:58
  5. 13.
    #24 Bayesian Computational Biology in Julia, with Seth Axen
    56:30
  6. 14.
    #23 Bayesian Stats in Business and Marketing Analytics, with Elea McDonnel Feit
    59:05
  7. 15.
    #22 Eliciting Priors and Doing Bayesian Inference at Scale, with Avi Bryant
    1:06:55
  8. 16.
    #21 Gaussian Processes, Bayesian Neural Nets & SIR Models, with Elizaveta Semenova
    1:02:11
  9. 17.
    #20 Regression and Other Stories, with Andrew Gelman, Jennifer Hill & Aki Vehtari
    1:03:44
  10. 18.
    #19 Turing, Julia and Bayes in Economics, with Cameron Pfiffer
    1:00:26
  11. 19.
    #SpecialAnnouncement: Patreon Launched!
    7:38
  12. 20.
    #18 How to ask good Research Questions and encourage Open Science, with Daniel Lakens
    58:31
  13. 21.
    #17 Reparametrize Your Models Automatically, with Maria Gorinova
    51:30
  14. 22.
    #16 Bayesian Statistics the Fun Way, with Will Kurt
    1:07:56
  15. 23.
    #15 The role of Python in Science and Education, with Michael Kennedy
    1:05:52
  16. 24.
    #14 Hidden Markov Models & Statistical Ecology, with Vianey Leos-Barajas
    49:05
  17. 25.
    #13 Building a Probabilistic Programming Framework in Julia, with Chad Scherrer
    43:55
  18. 26.
    #12 Biostatistics and Differential Equations, with Demetri Pananos
    46:30
  19. 27.
    #11 Taking care of your Hierarchical Models, with Thomas Wiecki
    58:07
  20. 28.
    #10 Exploratory Analysis of Bayesian Models, with ArviZ and Ari Hartikainen
    44:10
  21. 29.
    #9 Exploring the Cosmos with Bayes and Maggie Lieu
    53:50
  22. 30.
    #8 Bayesian Inference for Software Engineers, with Max Sklar
    48:46
  23. 31.
    #7 Designing a Probabilistic Programming Language & Debugging a Model, with Junpeng Lao
    45:42
  24. 32.
    #6 A principled Bayesian workflow, with Michael Betancourt
    1:03:52
  25. 33.
    #5 How to use Bayes in the biomedical industry, with Eric Ma
    46:41
  26. 34.
    #4 Dirichlet Processes and Neurodegenerative Diseases, with Karin Knudson
    49:33
  27. 35.
    #3.2 How to use Bayes in industry, with Colin Carroll
    32:09
  28. 36.
    #3.1 What is Probabilistic Programming & Why use it, with Colin Carroll
    32:36
  29. 37.
    #2 When should you use Bayesian tools, and Bayes in sports analytics, with Chris Fonnesbeck
    43:41
  30. 38.
    #1 Bayes, open-source and bioinformatics, with Osvaldo Martin
    49:43
  31. 39.
    #0 What is this podcast?
    12:18

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