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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
Sports Analytics
Variational Inference
Stan
Probabilistic Programming
Bayesian Deep Learning
Artificial Intelligence
Hierarchical Models
Covid-19
Gravitational Waves
Quantum Physics

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

PublishesWeeklyEpisodes205Founded7 years ago
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Artwork for Learning Bayesian Statistics

Latest Episodes

Today's clip is from Episode 158 featuring Stefan Radev. In this conversation, Alex Andorra and Stefan break down a core argument from their paper: Bayesian statistics has never been more computational than it is now, and simulation is the thread tha... more

Today's clip is from episode 159 featuring Matthijs Hollanders. In this conversation, Alex and Matthijs dig into a deceptively practical question: when you're modeling wildlife across space and time with Gaussian Processes, how do you keep the math f... 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 a Bayesian occ... more

Today's clip is from episode 158 featuring Stefan Radev. In this conversation, Alex and Stefan explore a genuinely fascinating problem: how do you turn an expert's intuition into a mathematically valid prior distribution - and can AI help automate th... more

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Recent Guests

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
Alana Karen
Author and former lead at Google specializing in tech leadership and culture.
Author of The Hard Tech Era
Episode: #149 The Future of Work in Tech, with Alana Karen
Scott Berry
Statistician and co-founder of Berry Consultants, known for innovative clinical trial designs
Berry Consultants
Episode: #148 Adaptive Trials, Bayesian Thinking, and Learning from Data, with Scott Berry

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 Kingdom15 days 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
    Italy25 days 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 Statesa month ago
  • Awesome show

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

    Apple Podcasts
    5
    Dr OfirGeva
    Israela month 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 Statesa month 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
#35
Saudi Arabia/Technology
Apple Podcasts
#100
Spain/Technology
Apple Podcasts
#211
Norway/Technology
Apple Podcasts
#215
South Korea/Technology
Apple Podcasts
#227
South Africa/Technology

Talking Points

Recent interactions between the hosts and their guests.

#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.
#159 Bayesian Occupancy Models, with Matthijs Hollanders
Q: If you had unlimited time and resources, which problem would you try to solve?
Habitat loss and the destruction of natural world around us, as addressing the drivers of biodiversity decline is the most pressing problem Matthijs would want to tackle if resources were unlimited.
#159 Bayesian Occupancy Models, with Matthijs Hollanders
Q: What is special about ARU data and why do traditional occupancy models struggle with it?
ARU data provide continuous monitoring with counts rather than discrete detections, so occupancy models that collapse data to binary detections discard valuable information about how many individuals were observed and how counts vary over time. This motivates modeling counts directly and using thinning to handle autocorrelation, enabling better inference about abundance proxies and detection dynamics.
#158 Bayesian Workflows & Foundation Models, with Stefan Radev
Q: Can foundation models realistically serve as the inference engine for Bayesian problems, or is the idea more about specialized, principled layers that call specific procedures?
Foundation models should act as an intelligent orchestration layer that delegates to dedicated numerical components (like specialized samplers) rather than trying to replace all numerical methods with a single end-to-end model. The practical path is to have a semantic layer that directs calls to appropriate inference engines (e.g., MCFC samplers) and to use training-time specialization rather than broad fine-tuning of large models.
#155 Probabilistic Programming for the Real World, with Andreas Munk
Q: How can practitioners start using PyProb today?
Begin by visiting the PyProb website and reviewing examples and documentation. Engage with maintainers, try out existing notebooks, and experiment with training simple inference networks within Python to understand amortized inference in probabilistic programs.

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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1. Super Data Science: ML & AI Podcast with Jon Krohn
2. The Joy of Why
3. Machine Learning Street Talk (MLST)
4. The Quanta Podcast
5. The Real Python Podcast

How many episodes of Learning Bayesian Statistics are there?

Learning Bayesian Statistics launched 7 years ago and published 205 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. Matthijs Hollanders
2. Stefan Radev
3. Adam Foster
4. Andreas Munk
5. Cherian Koshy
6. Daniel Saunders
7. Jonas Arruda
8. Alana Karen

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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