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Artwork for Learning Bayesian Statistics

Learning Bayesian Statistics

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

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

PublishesWeeklyEpisodes209Founded7 years ago
Number of ListenersCategories
TechnologyScience

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Artwork for Learning Bayesian Statistics

Latest Episodes

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: How does putting a Gau... more

Today's clip is from episode 161, featuring Luigi Acerbi. In this conversation, Luigi explains one of the biggest engineering bottlenecks facing transformer-based probabilistic models—and how his group found a way around it.

The core challenge is th... 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 Variational Bay... 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's the difference b... more

Key Facts

Accepts Guests
Contact Information
Podcast Host
Number of Listeners
Find out how many people listen to this podcast per episode and each month.

Recent Guests

Luigi Acerbi
Associate Professor of Artificial Human Intelligence at University of Helsinki; researcher in Bayesian inference and neural processes
University of Helsinki
Episode: #161 Amortized Inference & Neural Processes, with Luigi Acerbi
Vaden Masrani
Machine learning researcher turned consultant
Sophia AI Consulting
Episode: Bayesian Statistics vs Epistemology, with Vaden Masrani
Matthijs Hollanders
Postdoc focusing on wildlife data analysis and Bayesian models; creator of the Ocaroo package
University of Newcastle, Australia
Episode: #159 Bayesian Occupancy Models, with Matthijs Hollanders
Stefan Radev
Assistant professor at RPI, BayesOps lab founder, creator of BayesFlow
Rensselaer Polytechnic Institute (RPI) / BayesFlow
Episode: #157 Amortized Inference & BayesFlow in Practice, with Stefan Radev
Adam Foster
Researcher at Microsoft Research AI for Science working on Bayesian experimental design and related topics
Microsoft Research
Episode: #156 Bayesian Experimental Design & Active Learning, with Adam Foster
Andreas Munk
Founder of Evara, probabilistic programming researcher
Evara
Episode: #155 Probabilistic Programming for the Real World, with Andreas Munk
Cherian Koshy
VP at Kingslight; USA Today bestselling author of NeuroGiving
Kingslight
Episode: #153 The Neuroscience of Philanthropy, with Cherian Koshy
Daniel Saunders
Senior Data Scientist at PyMC Labs with a PhD in Philosophy
PyMC Labs
Episode: #152 A Bayesian decision theory workflow, with Daniel Saunders
Jonas Arruda
Mathematician and PhD researcher at the University of Bonn, key contributor to the BayesFlow Library.
University of Bonn
Episode: #151 Diffusion Models in Python, a Live Demo with Jonas Arruda

Host

Alex Andorra
Host of Learning Bayesian Statistics; senior data scientist and open-source contributor to PyMC and ArviZ.

Reviews

4.7 out of 5 stars from 250 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 Kingdom2 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
    Italy2 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 States2 months ago
  • Awesome show

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

    Apple Podcasts
    5
    Dr OfirGeva
    Israel2 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 States2 months ago

Listeners Say

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

Guests consistently bring actionable insights and real-world experience.
Some listeners wish for crisper editing and pacing, but content quality remains high.
Shows depth and practical value for Bayesian practitioners.

Chart Rankings

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

Apple Podcasts
#211
Canada/Technology
Apple Podcasts
#153
Philippines/Technology
Apple Podcasts
#155
Chile/Technology
Apple Podcasts
#179
Denmark/Technology
Apple Podcasts
#204
Japan/Technology
Apple Podcasts
#205
Netherlands/Technology

Talking Points

Recent interactions between the hosts and their guests.

#161 Amortized Inference & Neural Processes, with Luigi Acerbi
Q: Can you explain VBMC and how it differs from traditional Bayesian optimization?
VBMC uses a Gaussian-process surrogate to learn the full shape of the log-posterior, not just the location of a maximum. It aims to approximate the posterior with a limited number of evaluations, enabling more complete probabilistic insight in low-dimensional problems where evaluating the likelihood is costly.
#161 Amortized Inference & Neural Processes, with Luigi Acerbi
Q: What drew you to Bayesian statistics and amortized inference in the first place?
A long-running interest in modeling systems as probabilistic processes led me to Bayesian inference, then to methods that can scale learning across many datasets and tasks. Amortized inference emerged as a natural way to reuse learned representations across similar problems, turning expensive inference into fast predictions after training.
Bayesian Statistics vs Epistemology, with Vaden Masrani
Q: If you could have dinner with any great scientific mind, who would it be?
Richard Feynman, for his blend of scientific intellect, humanistic curiosity, humor, and love of life, which the guest believes would make for a rare and enriching conversation.
Bayesian Statistics vs Epistemology, with Vaden Masrani
Q: What's the elevator pitch for the Increments Podcast?
The show explores applied philosophy, science, and history around Popper, epistemology, AI, and current cultural trends, aiming to study how knowledge is formed and criticized across domains.
#159 Bayesian Occupancy Models, with Matthijs Hollanders
Q: If you could have dinner with any great scientific mind, who would it be?
Jeffrey West, author of Scale, for his big-picture perspective on scaling laws and how to think about complex systems scientifically.

Audience Metrics

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

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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 show focused on Bayesian statistics and probabilistic programming, with episodes that explore practical workflows, experiment design, and scalable inference. Listeners often engage with topics like simulation-based inference, amortized inference, diffusion models, and real-world applications in industry, neuroscience, and policy. Noteworthy is the emphasis on diagnostics, open-source tooling, and transparent discussions about failures and lessons learned, making it a valuable resource for practitioners who want actionable insights and tooling guidance from researchers and industry veterans alike.

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Which podcasts are similar to Learning Bayesian Statistics?

These podcasts share a similar audience with Learning Bayesian Statistics:

1. Super Data Science: ML & AI Podcast with Jon Krohn
2. The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
3. The Real Python Podcast
4. Machine Learning Street Talk (MLST)
5. DataFramed

How many episodes of Learning Bayesian Statistics are there?

Learning Bayesian Statistics launched 7 years ago and published 209 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. Luigi Acerbi
2. Vaden Masrani
3. Matthijs Hollanders
4. Stefan Radev
5. Adam Foster
6. Andreas Munk
7. Cherian Koshy
8. Daniel Saunders

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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