Learning Bayesian Statistics Podcast Por Alexandre Andorra arte de portada

Learning Bayesian Statistics

Learning Bayesian Statistics

De: Alexandre Andorra
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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. 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!

2025 Alexandre Andorra Ciencia
Episodios
  • BITESIZE | How Do Diffusion Models Work?

    BITESIZE | How Do Diffusion Models Work?
    Feb 19 2026

    Today's clip is from Episode 151 of the podcast, with Jonas Arruda

    In this conversation, Jonas Arruda explains how diffusion models generate data by learning to reverse a noise process. The idea is to start from a simple distribution like Gaussian noise and gradually remove noise until the target distribution emerges. This is done through a forward process that adds noise to clean parameters and a backward process that learns how to undo that corruption. A noise schedule controls how much noise is added or removed at each step, guiding the transformation from pure randomness back to meaningful structure.

    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/ !

    Más Menos
    4 m
  • 151 Diffusion Models in Python, a Live Demo with Jonas Arruda

    151 Diffusion Models in Python, a Live Demo with Jonas Arruda
    Feb 12 2026

    • Support & get perks!

    • Proudly sponsored by PyMC Labs! Get in touch at alex.andorra@pymc-labs.com

    • 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
    Más Menos
    1 h y 36 m
  • #150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik

    #150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik
    Jan 28 2026

    • Support & get perks!

    • Proudly sponsored by PyMC Labs! Get in touch at alex.andorra@pymc-labs.com

    • 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
    Más Menos
    1 h y 20 m
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