PodcastsEducationLearning Bayesian Statistics

Learning Bayesian Statistics

Alexandre Andorra
Learning Bayesian Statistics
Latest episode

188 episodes

  • Learning Bayesian Statistics

    151 Diffusion Models in Python, a Live Demo with Jonas Arruda

    2026/2/12 | 1h 35 mins.
    • Support & get perks!
    • Proudly sponsored by PyMC Labs! Get in touch at [email protected]
    • Intro to Bayes and Advanced Regression courses (first 2 lessons free)

    Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work !

    Chapters:
    00:00 Exploring Generative AI and Scientific Modeling
    10:27 Understanding Simulation-Based Inference (SBI) and Its Applications
    15:59 Diffusion Models in Simulation-Based Inference
    19:22 Live Coding Session: Implementing Baseflow for SBI
    34:39 Analyzing Results and Diagnostics in Simulation-Based Inference
    46:18 Hierarchical Models and Amortized Bayesian Inference
    48:14 Understanding Simulation-Based Inference (SBI) and Its Importance
    49:14 Diving into Diffusion Models: Basics and Mechanisms
    50:38 Forward and Backward Processes in Diffusion Models
    53:03 Learning the Score: Training Diffusion Models
    54:57 Inference with Diffusion Models: The Reverse Process
    57:36 Exploring Variants: Flow Matching and Consistency Models
    01:01:43 Benchmarking Different Models for Simulation-Based Inference
    01:06:41 Hierarchical Models and Their Applications in Inference
    01:14:25 Intervening in the Inference Process: Adding Constraints
    01:25:35 Summary of Key Concepts and Future Directions

    Thank you to my Patrons for making this episode possible!

    Links from the show:

    - Come meet Alex at the Field of Play Conference in Manchester, UK, March 27, 2026!
    - Jonas's Diffusion for SBI Tutorial & Review (Paper & Code)
    - The BayesFlow Library
    - Jonas on LinkedIn
    - Jonas on GitHub
    - Further reading for more mathematical details: Holderrieth & Erives
    - 150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik
    - 107 Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt
  • Learning Bayesian Statistics

    #150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik

    2026/1/28 | 1h 20 mins.
    • Support & get perks!
    • Proudly sponsored by PyMC Labs! Get in touch at [email protected]
    • Intro to Bayes and Advanced Regression courses (first 2 lessons free)

    Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work !

    Chapters:

    00:00 Scaling Bayesian Neural Networks
    04:26 Origin Stories of the Researchers
    09:46 Research Themes in Bayesian Neural Networks
    12:05 Making Bayesian Neural Networks Fast
    16:19 Microcanonical Langevin Sampler Explained
    22:57 Bottlenecks in Scaling Bayesian Neural Networks
    29:09 Practical Tools for Bayesian Neural Networks
    36:48 Trade-offs in Computational Efficiency and Posterior Fidelity
    40:13 Exploring High Dimensional Gaussians
    43:03 Practical Applications of Bayesian Deep Ensembles
    45:20 Comparing Bayesian Neural Networks with Standard Approaches
    50:03 Identifying Real-World Applications for Bayesian Methods
    57:44 Future of Bayesian Deep Learning at Scale
    01:05:56 The Evolution of Bayesian Inference Packages
    01:10:39 Vision for the Future of Bayesian Statistics

    Thank you to my Patrons for making this episode possible!

    Come meet Alex at the Field of Play Conference in Manchester, UK, March 27, 2026!

    Links from the show:

    David Rügamer:
    * Website
    * Google Scholar
    * GitHub

    Emanuel Sommer:
    * Website
    * GitHub
    * Google Scholar

    Jakob Robnik:
    * Google Scholar
    * GitHub
    * Microcanonical Langevin paper
    * LinkedIn
  • Learning Bayesian Statistics

    BITESIZE | Building Resilience in Modern Tech Careers

    2026/1/21 | 25 mins.
    Today’s clip is from episode 149 of the podcast, with Alana Karen.

    This conversation explores the evolving landscape of technology, particularly in Silicon Valley, focusing on the cultural shifts due to mass layoffs, the debate over remote work, and the impact of AI on job roles and priorities. The discussion highlights the importance of adapting to these changes and preparing for the future by developing complex skills that AI cannot easily replicate.

    Get the full discussion here!

    • Join this channel to get access to perks:
    https://www.patreon.com/c/learnbayesstats

    • Intro to Bayes Course (first 2 lessons free): https://topmate.io/alex_andorra/503302
    • Advanced Regression Course (first 2 lessons free): https://topmate.io/alex_andorra/1011122

    Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !
  • Learning Bayesian Statistics

    #149 The Future of Work in Tech, with Alana Karen

    2026/1/14 | 1h 32 mins.
    • Support & get perks!
    • Proudly sponsored by PyMC Labs! Get in touch at [email protected]
    • Intro to Bayes and Advanced Regression courses (first 2 lessons free)

    Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work !

    Chapters:
    11:37 The Hard Tech Era
    21:08 The Shift in Tech Work Culture
    28:49 AI's Impact on Job Security and Work Dynamics
    34:33 Adapting to AI: Skills for the Future
    45:56 Understanding AI Models and Their Limitations
    47:25 The Importance of Diversity in AI Development
    54:34 Positioning Technical Talent for Job Security
    57:58 Building Resilience in Uncertain Times
    01:06:33 Recognizing Diverse Ambitions in Career Progression
    01:12:51 The Role of Managers in Employee Retention
    01:26:55 Solving Complex Problems with AI and Innovation

    Thank you to my Patrons for making this episode possible!

    Links from the show:
    Alana's latest book (Use code BAYESIAN for 10% off + a free interview preparation download PDF)
    Alana’s Substack
    Alana on Linkedin
    Alana on Instagram
    The Obstacle Is the Way – The Timeless Art of Turning Trials into Triumph
    Courage Is Calling – Fortune Favours the Brave
  • Learning Bayesian Statistics

    BITESIZE | The Trial Design That Learns in Real Time

    2026/1/07 | 22 mins.
    Today’s clip is from episode 148 of the podcast, with Scott Berry.

    In this conversation, Alex and Scott discuss emphasizing the shift from frequentist to Bayesian approaches in clinical trials.

    They highlight the limitations of traditional trial designs and the advantages of adaptive and platform trials, particularly in the context of COVID-19 treatment.

    The discussion provides insights into the complexities of trial design and the innovative methodologies that are shaping the future of medical research.

    Get the full discussion here!

    • Join this channel to get access to perks: https://www.patreon.com/c/learnbayesstats

    • Intro to Bayes Course (first 2 lessons free): https://topmate.io/alex_andorra/503302
    • Advanced Regression Course (first 2 lessons free): https://topmate.io/alex_andorra/1011122

    Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

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About Learning Bayesian Statistics

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. By day, I'm a Senior data scientist. 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 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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