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, and I live in Estonia. By day, I'm a data scientist and modeler at the https://www.pymc-labs.io/ (PyMC Labs) consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/ (ArviZ). I also love https://www.pollsposition.com/ (election forecasting) and, most importantly, 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 https://www.patreon.com/learnbayesstats (unlock exclusive Bayesian swag on Patreon)!
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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
BART as a core tool: Gabriel explains how Bayesian Additive Regression Trees provide robust uncertainty quantification and serve as a reliable baseline model in many domains.
Rust for performance: His Rust re-implementation of BART dramatically improves speed and scalability, making it feasible for larger datasets and real-world IoT applications.
Strengths and trade-offs: BART avoids overfitting and handles missing data gracefully, though it is slower than other tree-based approaches.
Big data meets Bayes: Gabriel shares strategies for applying Bayesian methods with big data, including when variational inference helps balance scale with rigor.
Optimization and decision-making: He highlights how BART models can be embedded into optimization frameworks, opening doors for sequential decision-making.
Open source matters: Gabriel emphasizes the importance of communities like PyMC and Bambi, encouraging newcomers to start with small contributions.
Chapters:
05:10 – From economics to IoT and Bayesian statistics
18:55 – Introduction to BART (Bayesian Additive Regression Trees)
24:40 – Re-implementing BART in Rust for speed and scalability
32:05 – Comparing BART with Gaussian Processes and other tree methods
39:50 – Strengths and limitations of BART
47:15 – Handling missing data and different likelihoods
54:30 – Variational inference and big data challenges
01:01:10 – Embedding BART into optimization and decision-making frameworks
01:08:45 – Open source, PyMC, and community support
01:15:20 – Advice for newcomers
01:20:55 – Future of BART, Rust, and probabilistic programming
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian...
Today’s clip is from episode 141 of the podcast, with Sam Witty.
Alex and Sam discuss the ChiRho project, delving into the intricacies of causal inference, particularly focusing on Do-Calculus, regression discontinuity designs, and Bayesian structural causal inference.
They explain ChiRho's design philosophy, emphasizing its modular and extensible nature, and highlights the importance of efficient estimation in causal inference, making complex statistical methods accessible to users without extensive expertise.
Today’s clip is from episode 140 of the podcast, with Ron Yurko.
Alex and Ron discuss the challenges of model deployment, and the complexities of modeling player contributions in team sports like soccer and football.
They emphasize the importance of understanding replacement levels, the Going Deep framework in football analytics, and the need for proper modeling of expected points.
Additionally, they share insights on teaching Bayesian modeling to students and the difficulties they face in grasping the concepts of model writing and application.
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
Teaching students to write out their own models is crucial.
Developing a sports analytics portfolio is essential for aspiring analysts.
Modeling expectations in sports analytics can be misleading.
Tracking data can significantly improve player performance models.
Ron encourages students to engage in active learning through projects.
The importance of understanding the dependency structure in data is vital.
Ron aims to integrate more diverse sports analytics topics into his teaching.
Chapters:
BITESIZE | Is Bayesian Optimization the Answer?
mercredi 27 août 2025 • Durée 25:13
Today’s clip is from episode 139 of the podcast, with with Max Balandat.
Alex and Max discuss the integration of BoTorch with PyTorch, exploring its applications in Bayesian optimization and Gaussian processes. They highlight the advantages of using GPyTorch for structured matrices and the flexibility it offers for research.
The discussion also covers the motivations behind building BoTorch, the importance of open-source culture at Meta, and the role of PyTorch in modern machine learning.
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
BoTorch is designed for researchers who want flexibility in Bayesian optimization.
The integration of BoTorch with PyTorch allows for differentiable programming.
Scalability at Meta involves careful software engineering practices and testing.
Open-source contributions enhance the development and community engagement of BoTorch.
LLMs can help incorporate human knowledge into optimization processes.
Max emphasizes the importance of clear communication of uncertainty to stakeholders.
The role of a researcher in industry is often more application-focused than in academia.
Max's team at Meta works on adaptive experimentation and Bayesian optimization.
Chapters:
08:51 Understanding BoTorch
12:12 Use Cases and Flexibility of BoTorch
BITESIZE | What's Missing in Bayesian Deep Learning?
mercredi 13 août 2025 • Durée 20:34
Today’s clip is from episode 138 of the podcast, with Mélodie Monod, François-Xavier Briol and Yingzhen Li.
During this live show at Imperial College London, Alex and his guests delve into the complexities and advancements in Bayesian deep learning, focusing on uncertainty quantification, the integration of machine learning tools, and the challenges faced in simulation-based inference.
The speakers discuss their current projects, the evolution of Bayesian models, and the need for better computational tools in the field.
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
Bayesian deep learning is a growing field with many challenges.
Current research focuses on applying Bayesian methods to neural networks.
Diffusion methods are emerging as a new approach for uncertainty quantification.
The integration of machine learning tools into Bayesian models is a key area of research.
The complexity of Bayesian neural networks poses significant computational challenges.
Future research will focus on improving methods for uncertainty quantification. Generalized Bayesian inference offers a more robust approach to uncertainty.
Uncertainty quantification is crucial in fields like medicine and epidemiology.
Detecting out-of-distribution examples is essential for model reliability.
Exploration-exploitation trade-off is vital in reinforcement learning.
Marginal likelihood can be misleading for model selection.
BITESIZE | Practical Applications of Causal AI with LLMs, with Robert Ness
mercredi 30 juillet 2025 • Durée 25:28
Today’s clip is from episode 137 of the podcast, with Robert Ness.
Alex and Robert discuss the intersection of causal inference and deep learning, emphasizing the importance of understanding causal concepts in statistical modeling.
The discussion also covers the evolution of probabilistic machine learning, the role of inductive biases, and the potential of large language models in causal analysis, highlighting their ability to translate natural language into formal causal queries.
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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17:55 Counterfactual Worlds and Mediation Analysis
20:47 Efficient Estimation in ChiRho
24:08 Future Directions for Causal AI
50:21 Understanding the Do-Operator in Causal Inference
56:45 ChiRho’s Role in Causal Inference and Bayesian Modeling
01:01:36 Roadmap and Future Developments for ChiRho
01:05:29 Real-World Applications of Causal Probabilistic Programming
01:10:51 Challenges in Causal Inference Adoption
01:11:50 The Importance of Causal Claims in Research
01:18:11 Bayesian Approaches to Causal Inference
01:22:08 Combining Gaussian Processes with Causal Inference
01:28:27 Future Directions in Probabilistic Programming and Causal Inference
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad...
03:51 The Journey into Sports Analytics
15:20 The Evolution of Bayesian Statistics in Sports
26:01 Innovations in NFL WAR Modeling
39:23 Causal Modeling in Sports Analytics
46:29 Defining Replacement Levels in Sports
48:26 The Going Deep Framework and Big Data in Football
52:47 Modeling Expectations in Football Data
55:40 Teaching Statistical Concepts in Sports Analytics
01:01:54 The Importance of Model Building in Education
01:04:46 Statistical Thinking in Sports Analytics
01:10:55 Innovative Research in Player Movement
01:15:47 Exploring Data Needs in American Football
01:18:43 Building a Sports Analytics Portfolio
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell,...
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Réduire
15:02 Integration with PyTorch and GPyTorch
17:57 Practical Applications of BoTorch
20:50 Open Source Culture at Meta and BoTorch's Development
43:10 The Power of Open Source Collaboration
47:49 Scalability Challenges at Meta
51:02 Balancing Depth and Breadth in Problem Solving
55:08 Communicating Uncertainty to Stakeholders
01:00:53 Learning from Missteps in Research
01:05:06 Integrating External Contributions into BoTorch
01:08:00 The Future of Optimization with LLMs
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode,...
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The integration of Bayesian methods in LLMs presents unique challenges.
Chapters:
00:00 Introduction to Bayesian Deep Learning
03:12 Panelist Introductions and Backgrounds
10:37 Current Research and Challenges in Bayesian Deep Learning
18:04 Contrasting Approaches: Bayesian vs. Machine Learning
26:09 Tools and Techniques for Bayesian Deep Learning
31:18 Innovative Methods in Uncertainty Quantification
36:23 Generalized Bayesian Inference and Its Implications
41:38 Robust Bayesian Inference and Gaussian Processes
44:24 Software Development in Bayesian Statistics
46:51 Understanding Uncertainty in Language Models
50:03 Hallucinations in Language Models
53:48 Bayesian Neural Networks vs Traditional Neural Networks
58:00 Challenges with Likelihood Assumptions
01:01:22 Practical Applications of Uncertainty Quantification
01:04:33 Meta Decision-Making with Uncertainty
01:06:50 Exploring Bayesian Priors in Neural Networks
01:09:17 Model Complexity and Data Signal
01:12:10 Marginal Likelihood and Model Selection
01:15:03 Implementing Bayesian Methods in LLMs
01:19:21 Out-of-Distribution Detection in LLMs
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer,...