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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 Science
Épisodes
  • Bitesize | How To Model Risk Aversion In Pricing?

    Bitesize | How To Model Risk Aversion In Pricing?
    Mar 4 2026

    Today's clip is from Episode 152 of the podcast, with Daniel Saunders.

    In this conversation, Daniel Saunders explains how to incorporate risk aversion into Bayesian price optimization. The key insight is that uncertainty around expected profit is asymmetric across price points, low prices yield more predictable (if modest) returns, while high prices introduce much wider uncertainty. Rather than simply maximizing expected profit, you can pass profit through an exponential utility function that models diminishing returns, a well-established idea from economics.

    This adds an adjustable risk aversion parameter to the optimization: as risk aversion increases, the model shifts toward more conservative price recommendations, trading off potentially large but uncertain gains for outcomes with tighter, more reliable distributions.

    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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    4 min
  • #152 A Bayesian decision theory workflow, with Daniel Saunders

    #152 A Bayesian decision theory workflow, with Daniel Saunders
    Feb 26 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 The Importance of Decision-Making in Data Science

    06:41 From Philosophy to Bayesian Statistics

    14:57 The Role of Soft Skills in Data Science

    18:19 Understanding Decision Theory Workflows

    22:43 Shifting Focus from Accuracy to Business Value

    26:23 Leveraging PyTensor for Optimization

    34:27 Applying Optimal Decision-Making in Industry

    40:06 Understanding Utility Functions in Regulation

    41:35 Introduction to Obeisance Decision Theory Workflow

    42:33 Exploring Price Elasticity and Demand

    45:54 Optimizing Profit through Bayesian Models

    51:12 Risk Aversion and Utility Functions

    57:18 Advanced Risk Management Techniques

    01:01:08 Practical Applications of Bayesian Decision-Making

    01:06:54 Future Directions in Bayesian Inference

    01:10:16 The Quest for Better Inference Algorithms

    01:15:01 Dinner with a Polymath: Herbert Simon

    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! https://www.fieldofplay.co.uk/

    • A Bayesian decision theory workflow
    • Daniel's website, LinkedIn and GitHub
    • LBS #124 State Space Models & Structural Time Series, with Jesse Grabowski
    • LBS #123 BART & The Future of Bayesian Tools, with Osvaldo Martin
    • LBS #74 Optimizing NUTS and Developing the ZeroSumNormal Distribution, with Adrian Seyboldt
    • LBS #76 The Past, Present & Future of Stan, with Bob Carpenter
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    1 h et 19 min
  • 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/ !

    Afficher plus Afficher moins
    4 min
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