あらすじ・解説

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
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  • #129 Bayesian Deep Learning & AI for Science with Vincent Fortuin

    #129 Bayesian Deep Learning & AI for Science with Vincent Fortuin
    2025/04/02

    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:

    • The hype around AI in science often fails to deliver practical results.
    • Bayesian deep learning combines the strengths of deep learning and Bayesian statistics.
    • Fine-tuning LLMs with Bayesian methods improves prediction calibration.
    • There is no single dominant library for Bayesian deep learning yet.
    • Real-world applications of Bayesian deep learning exist in various fields.
    • Prior knowledge is crucial for the effectiveness of Bayesian deep learning.
    • Data efficiency in AI can be enhanced by incorporating prior knowledge.
    • Generative AI and Bayesian deep learning can inform each other.
    • The complexity of a problem influences the choice between Bayesian and traditional deep learning.
    • Meta-learning enhances the efficiency of Bayesian models.
    • PAC-Bayesian theory merges Bayesian and frequentist ideas.
    • Laplace inference offers a cost-effective approximation.
    • Subspace inference can optimize parameter efficiency.
    • Bayesian deep learning is crucial for reliable predictions.
    • Effective communication of uncertainty is essential.
    • Realistic benchmarks are needed for Bayesian methods
    • Collaboration and communication in the AI community are vital.

    Chapters:

    00:00 Introduction to Bayesian Deep Learning

    04:24 Vincent Fortuin’s Journey to Bayesian Deep Learning

    11:52 Understanding Bayesian Deep Learning

    16:29 Current Landscape of Bayesian Libraries

    21:11 Real-World Applications of Bayesian Deep Learning

    23:33 When to Use Bayesian Deep Learning

    28:22 Data Efficiency in AI and Generative Modeling

    30:18 Integrating Bayesian Knowledge into Generative Models

    31:44 The Role of Meta-Learning in Bayesian Deep Learning

    34:06 Understanding Pack Bayesian Theory

    37:55 Algorithms for Bayesian Deep Learning Models
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    1 時間
  • #128 Building a Winning Data Team in Football, with Matt Penn

    #128 Building a Winning Data Team in Football, with Matt Penn
    2025/03/19

    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:

    • Matt emphasizes the importance of Bayesian statistics in scenarios with limited data.
    • Communicating insights to coaches is a crucial skill for data analysts.
    • Building a data team requires understanding the needs of the coaching staff.
    • Player recruitment is a significant focus in football analytics.
    • The integration of data science in sports is still evolving.
    • Effective data modeling must consider the practical application in games.
    • Collaboration between data analysts and coaches enhances decision-making.
    • Having a robust data infrastructure is essential for efficient analysis.
    • The landscape of sports analytics is becoming increasingly competitive.
    • Player recruitment involves analyzing various data models.
    • Biases in traditional football statistics can skew player evaluations.
    • Statistical techniques should leverage the structure of football data.
    • Tracking data opens new avenues for understanding player movements.
    • The role of data analysis in football will continue to grow.
    • Aspiring analysts should focus on curiosity and practical experience.

    Chapters:

    00:00 Introduction to Football Analytics and Matt's Journey

    04:54 The Role of Bayesian Methods in Football

    10:20 Challenges in Communicating Data Insights

    17:03 Building Relationships with Coaches

    22:09 The Structure of the Data Team at Como

    26:18 Focus on Player Recruitment and Transfer Strategies

    28:48 January Transfer Window Insights

    30:54 Biases in Football Data Analysis

    34:11 Comparative Analysis of Men's and Women's Football

    36:55 Statistical Techniques in Football Analysis

    42:48 The Impact of Tracking Data on Football Analysis

    45:49 The Future of Data-Driven Football Strategies

    47:27 Advice for Aspiring Football Analysts
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    58 分
  • #127 Saving Sharks... with Python, Causal Inference and Aaron MacNeil

    #127 Saving Sharks... with Python, Causal Inference and Aaron MacNeil
    2025/03/05

    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 ;)

    Thank you to my Patrons for making this episode possible!

    Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, 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, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia and Michael Cao.

    Takeaways:

    • Sharks play a crucial role in maintaining healthy ocean ecosystems.
    • Bayesian statistics are particularly useful in data-poor environments like ecology.
    • Teaching Bayesian statistics requires a shift in mindset from traditional statistical methods.
    • The shark meat trade is significant and often overlooked.
    • Ray meat trade is as large as shark meat trade, with specific markets dominating.
    • Understanding the ecological roles of species is essential for effective conservation.
    • Causal language is important in ecological research and should be encouraged.
    • Evidence-driven decision-making is crucial in balancing human and ecological needs.
    • Expert opinions are...
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    1 時間 4 分

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