Learning Bayesian Statistics Podcast Por Alexandre Andorra capa

Learning Bayesian Statistics

Learning Bayesian Statistics

De: Alexandre Andorra
Ouça grátis

Sobre este áudio

 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 Ciências
Episódios
  • #140 NFL Analytics & Teaching Bayesian Stats, with Ron Yurko

    #140 NFL Analytics & Teaching Bayesian Stats, with Ron Yurko
    Sep 3 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:

    • Teaching students to write out their own models is crucial.
    • Developing a sports analytics portfolio is essential for aspiring analysts.
    • Modeling expectations in sports analytics can be misleading.
    • Tracking data can significantly improve player performance models.
    • Ron encourages students to engage in active learning through projects.
    • The importance of understanding the dependency structure in data is vital.
    • Ron aims to integrate more diverse sports analytics topics into his teaching.

    Chapters:

    03:51 The Journey into Sports Analytics

    15:20 The Evolution of Bayesian Statistics in Sports

    26:01 Innovations in NFL WAR Modeling

    39:23 Causal Modeling in Sports Analytics

    46:29 Defining Replacement Levels in Sports

    48:26 The Going Deep Framework and Big Data in Football

    52:47 Modeling Expectations in Football Data

    55:40 Teaching Statistical Concepts in Sports Analytics

    01:01:54 The Importance of Model Building in Education

    01:04:46 Statistical Thinking in Sports Analytics

    01:10:55 Innovative Research in Player Movement

    01:15:47 Exploring Data Needs in American Football

    01:18:43 Building a Sports Analytics Portfolio

    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,...
    Exibir mais Exibir menos
    1 hora e 33 minutos
  • BITESIZE | Is Bayesian Optimization the Answer?

    BITESIZE | Is Bayesian Optimization the Answer?
    Aug 27 2025

    Today’s clip is from episode 139 of the podcast, with with Max Balandat.

    Alex and Max discuss the integration of BoTorch with PyTorch, exploring its applications in Bayesian optimization and Gaussian processes. They highlight the advantages of using GPyTorch for structured matrices and the flexibility it offers for research.

    The discussion also covers the motivations behind building BoTorch, the importance of open-source culture at Meta, and the role of PyTorch in modern machine learning.

    Get the full discussion 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.
    Exibir mais Exibir menos
    25 minutos
  • #139 Efficient Bayesian Optimization in PyTorch, with Max Balandat

    #139 Efficient Bayesian Optimization in PyTorch, with Max Balandat
    Aug 20 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:

    • BoTorch is designed for researchers who want flexibility in Bayesian optimization.
    • The integration of BoTorch with PyTorch allows for differentiable programming.
    • Scalability at Meta involves careful software engineering practices and testing.
    • Open-source contributions enhance the development and community engagement of BoTorch.
    • LLMs can help incorporate human knowledge into optimization processes.
    • Max emphasizes the importance of clear communication of uncertainty to stakeholders.
    • The role of a researcher in industry is often more application-focused than in academia.
    • Max's team at Meta works on adaptive experimentation and Bayesian optimization.

    Chapters:

    08:51 Understanding BoTorch

    12:12 Use Cases and Flexibility of BoTorch

    15:02 Integration with PyTorch and GPyTorch

    17:57 Practical Applications of BoTorch

    20:50 Open Source Culture at Meta and BoTorch's Development

    43:10 The Power of Open Source Collaboration

    47:49 Scalability Challenges at Meta

    51:02 Balancing Depth and Breadth in Problem Solving

    55:08 Communicating Uncertainty to Stakeholders

    01:00:53 Learning from Missteps in Research

    01:05:06 Integrating External Contributions into BoTorch

    01:08:00 The Future of Optimization with LLMs

    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,...
    Exibir mais Exibir menos
    1 hora e 25 minutos
Ainda não há avaliações