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Artwork for Learning Bayesian Statistics

Learning Bayesian Statistics

Alexandre Andorra
Bayesian Statistics
Bayesian Inference
Machine Learning
Gaussian Processes
Causal Inference
Pymc
Statistical Modeling
Stan
Data Science
Uncertainty Quantification
Bayesian Methods
Artificial Intelligence
Sports Analytics
Variational Inference
Probabilistic Programming
Bayesian Deep Learning
Hierarchical Models
Simulation-Based Inference
Diffusion Models
Covid-19

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 learnin... more

PublishesWeeklyEpisodes225Founded7 years ago
Number of ListenersCategories
TechnologyScience

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Artwork for Learning Bayesian Statistics

Latest Episodes

This one is a bonus episode, where I was the guest, on a podcast called One Thing That Worked! Get all the insights and takeaways here. In the meantime, here is how they described it:

In this insightful episode of One Thing That Worked, my guest is ... more

Today’s clip is from Episode 167 of Learning Bayesian Statistics, featuring Paul-Christian Bürkner and Steve Bronder, recorded live at StanCon 2026 in Uppsala, Sweden.

Paul Bürkner, creator of brms and a leading researcher behind BayesFlow, explains... more

Support & Resources

→ Support the show on Patreon

→ Bayesian Modeling 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!

Takeaways:

Q: What is amortized Bay... more

Today's clip is from Episode 166, featuring Bill Engels & Jesse Grabowski. In this conversation, Bill explains what a kernel means in a Gaussian process and how it defines the idea of similarity between data points. He then breaks down the role of th... more

Key Facts

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Contact Information
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Find out how many people listen to this podcast per episode and each month.

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Recent Guests

Paul-Christian Bürkner
Creator of brms and a leading researcher behind BayesFlow
Episode: Can Neural Networks Replace MCMC?
Steve Bronder
Episode: Can Neural Networks Replace MCMC?
Bill Engels
GP developer and PyMC contributor who wrote the original Gaussian Process submodule for PyMC
PyMC community
Episode: #166 PTGP: A New Gaussian Process Library, with Bill Engels & Jesse Grabowski
Jesse Grabowski
Returning guest with background in state space models and Kalman filters; contributed to PyTensor rewrites and PTGP design
PyTensor team
Episode: #166 PTGP: A New Gaussian Process Library, with Bill Engels & Jesse Grabowski
Alex Fengler
Brown University postdoctoral researcher and lead developer of HSSM (Hierarchical Sequential Sampling Models)
Brown University
Episode: #165 Hierarchical Sequential Sampling Modeling, with Alex Fengler
Dorota Vojcik
Grand prize contest winner presenting her bat mortality problem
Poland / Forest Research Institute context
Episode: #164 Bayesian Workflow, with Andrew Gelman, Aki Vehtari & Richard McElreath
Adrian Seyboldt
Researcher focused on preconditioning and Nutpie development
Flatiron Institute / PyMC Labs (via Bob Carpenter)
Episode: #163 How to make your models sample faster, with Adrian Seyboldt & Eliot Carlson
Eliot Carlson
Research analyst in Bob Carpenter's group at the Flatiron Institute, co-author on the preconditioning work
Flatiron Institute
Episode: #163 How to make your models sample faster, with Adrian Seyboldt & Eliot Carlson
Christopher Krapu
Scientist at Nvidia focusing on retrieval and agentic AI tools; long-time PyMC contributor with a background in hydrology and environmental science
Nvidia
Episode: #162 Bayesian Hydrology & GPU AI, with Christopher Krapu

Host

Alex Andorra
Host of Learning Bayesian Statistics

Reviews

4.7 out of 5 stars from 254 ratings
  • Great resource

    Amazing podcast, there is hardly an episode that doesn’t send me down a new Bayesian rabbit hole!

    Apple Podcasts
    5
    Hessam Mehr
    United Kingdom5 months ago
  • Insightful podcast

    I am a newbie and I was introduced to this podcast recently. Highly recommend if you want to learn techniques and future trends (and 1001 stories around Bayesian stats) from practitioners and professionals!

    Apple Podcasts
    5
    Chi_Tuan
    Italy5 months ago
  • Best way to learn about cutting edge Bayesian statistics!

    A fantastic podcast for anyone interested in Bayesian statistics. When I hear the fun theme song I know I am about to learn something new from Alex and his heavy hitter guests! I first learned about things like amortized Bayesian inference and Nutpie from this podcast, and several episodes have given me ideas that were immediately applicable to my own work.

    Highly recommended for anyone who wants to keep up with modern applied Bayesian methods.

    Apple Podcasts
    5
    DrAleator
    United States5 months ago
  • Awesome show

    Diverse guests from many aspects of the bayesian ecosystem and intelligent interviews. Recommended!

    Apple Podcasts
    5
    Dr OfirGeva
    Israel5 months ago
  • Great show for Bayesian intuition

    The Frank Harrell episode is what gave me the epiphany and intuition about what makes Bayesian thinking so superior. Highly recommended.

    Apple Podcasts
    5
    zajichekstats
    United States5 months ago

Listeners Say

Key themes from listener reviews, highlighting what works and what could be improved about the show.

Some listeners note the host's energy and the show's ability to connect theory to practice.
Guests are consistently described as heavy-hitting and knowledgeable.
Listeners appreciate the practical focus on Bayesian workflows and real-world applications.

Chart Rankings

How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.

Apple Podcasts
#104
South Korea/Technology
Apple Podcasts
#155
Singapore/Technology
Apple Podcasts
#177
Poland/Technology

Talking Points

Recent interactions between the hosts and their guests.

How I built Learn Bayes Stats to the Top 1.5%
Q: What will be your main lesson? To share a practical lesson, something that people can do.
He recommends creating the show you wish existed to solve a problem you have, identifying the audience you want to help, and using failure and feedback to improve.
How I built Learn Bayes Stats to the Top 1.5%
Q: What would you say is the one thing, one decision or habit or strategy that boosted this show, this podcast the most?
Andorra says consistent publishing every two weeks since 2019 has helped build a routine for both him and his listeners.
#167 Amortized Inference & GPUs for Stan, with Paul-Christian Bürkner & Steve Bronder
Q: Should we be concerned that users of Stan and brms may not understand the algorithms underlying the software, and how should we balance ease of use with teaching users to think more deeply?
Bronder compares software abstractions to compilers: users should be able to inspect what happens, but may not need to write every low-level component. Bürkner says the right level of understanding depends on the audience and their intended use.
#167 Amortized Inference & GPUs for Stan, with Paul-Christian Bürkner & Steve Bronder
Q: Does the posterior in simulation-based inference widen or contract depending on how much training data you give your neural network?
The effect depends on the loss function and architecture. With forward KL loss, undertrained posteriors often overestimate uncertainty or fail to learn how data informs certain parameters; reverse KL can instead miss modes and underestimate uncertainty.
Can Neural Networks Replace MCMC?
Q: Are there methods or measures you can use to tell when this didn't quite work as wanted?
Bürkner explains that simulation-based calibration tests performance on simulated data but may not reveal failures on out-of-distribution real-world data. His group is developing diagnostics that use networks to summarize data and detect when those summaries suggest an unreliable posterior approximation.

Audience Metrics

Listeners, social reach, demographics and more for this podcast.

Listeners per Episode
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Frequently Asked Questions About Learning Bayesian Statistics

What is Learning Bayesian Statistics about and what kind of topics does it cover?

A technically rich program focused on Bayesian methods across science and industry, with guests ranging from ecological modeling to AI systems and probabilistic programming. Listeners are treated to practical workflows, tooling tips, and discussions about how Bayesian thinking informs model design, uncertainty quantification, and decision making in real-world settings. The show frequently features deep dives into computational strategies, prior elicitation, and scalable inference, often balancing rigorous theory with actionable guidance. A notable strength is the willingness to critique methods and share failures alongside successes, making it useful for practitioners who want to apply Bayesian statistics and stay current with the ecosystem... more

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2. Super Data Science: ML & AI Podcast with Jon Krohn
3. EconTalk
4. Dwarkesh Podcast
5. Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas

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Learning Bayesian Statistics launched 7 years ago and published 225 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on Learning Bayesian Statistics?

Recent guests on Learning Bayesian Statistics include:

1. Paul-Christian Bürkner
2. Steve Bronder
3. Bill Engels
4. Jesse Grabowski
5. Alex Fengler
6. Dorota Vojcik
7. Adrian Seyboldt
8. Eliot Carlson

To view more recent guests and their details, simply upgrade your Rephonic account. You'll also get access to a typical guest profile to help you decide if the show is worth pitching.

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