Learning Bayesian Statistics
Alexandre Andorra
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)!
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)!
Episodes
Mentioned books
14 snips
Oct 15, 2025 • 1h 13min
#143 Transforming Nutrition Science with Bayesian Methods, with Christoph Bamberg
In this discussion, Christoph Bamberg, a researcher in cognitive science and health psychology, explores the impact of Bayesian methods on nutrition science. He shares insights on how dietary framing can influence cognition, revealing that effects of intermittent fasting depend on context and individual rhythms. Christoph emphasizes the importance of clear definitions in research and how small effects can have significant public health implications. He also highlights the challenges of converting collaborators to Bayesian methods and offers advice for students diving into this complex field.
Oct 9, 2025 • 23min
BITESIZE | How Bayesian Additive Regression Trees Work in Practice
Gabriel Stechschulte, a Bayesian software developer known for his work with PyMCBART, dives into the re-implementation of Bayesian Additive Regression Trees (BART) in Rust. He discusses the technical hurdles and enhanced performance achieved through this project. Gabriel explains the value of BART in uncertainty quantification and how it contrasts with other tree-based methods. The conversation also covers practical aspects, like integrating BART with Python and balancing open-source contributions with a full-time job, all while exploring the innovative features of PyMCBART.
10 snips
Oct 2, 2025 • 1h 10min
#142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte
Gabriel Stechschulte is a software engineer specializing in Bayesian methods and optimization. He discusses the power of Bayesian Additive Regression Trees (BART) for uncertainty quantification and its re-implementation in Rust, enhancing performance for big data. Gabriel explores how BART contrasts with other models, its strengths in avoiding overfitting, and its integration into optimization frameworks for decision-making. He also emphasizes the importance of open-source communities, encouraging newcomers to contribute actively.
8 snips
Sep 24, 2025 • 22min
BITESIZE | How Probability Becomes Causality?
In this engaging discussion, Sam Witty, a researcher from the Cairo project, dives into the fascinating world of causal inference. He explains the differences between do-calculus and Cairo’s parametric Bayesian methods, and how regression discontinuity designs enable causal estimation. Sam also explores how Cairo automates the construction of interventions, providing users easy access to complex statistical tools. The talk highlights the significance of efficient estimators, making causal queries more accessible without needing extensive expertise.
35 snips
Sep 18, 2025 • 1h 38min
#141 AI Assisted Causal Inference, with Sam Witty
In this engaging discussion, Sam Whitty, the founder of Sorbus AI and a pioneer in causal probabilistic programming, dives into the intricacies of causal inference. He explores his journey from engineering to developing ChiRho, a language that merges mechanistic and data-driven models. Listeners will learn about counterfactual reasoning, the significance of modular design, and practical applications in science and engineering. Sam emphasizes the need for collaboration in transforming causal questions into actionable insights, while also looking ahead at the future of causal AI.
19 snips
Sep 10, 2025 • 24min
BITESIZE | How to Think Causally About Your Models?
In this discussion, Ron Yurko, an expert in sports analytics, shares insights on the complexities of modeling player contributions in soccer and football. He highlights the significance of understanding replacement levels and introduces the Going Deep framework for analyzing player performance. They touch on the challenges of teaching Bayesian modeling, particularly how students struggle with model writing. The conversation underscores the importance of using advanced tracking data for better predictions and the necessity of viewing entire distributions in utility function modeling.
9 snips
Sep 3, 2025 • 1h 33min
#140 NFL Analytics & Teaching Bayesian Stats, with Ron Yurko
Ron Yurko, an Assistant Teaching Professor and Director of Sports Analytics at Carnegie Mellon University, shares his expertise in Bayesian statistics applied to NFL analytics. He emphasizes the significance of teaching students model-building skills and engaging them in practical projects. The discussion highlights challenges in player performance modeling, the impact of tracking data, and the evolving curriculum in sports analytics education. Ron also advocates for developing a robust sports analytics portfolio to help aspiring analysts thrive in the industry.
7 snips
Aug 27, 2025 • 25min
BITESIZE | Is Bayesian Optimization the Answer?
In this discussion, Max Balandat, a key figure in Bayesian optimization and an advocate for open-source culture at Meta, shares insights on the integration of BoTorch with PyTorch. He highlights the flexibility and user-friendly nature of GPyTorch for handling optimization challenges with large datasets. Max explores the advantages of using neural networks as feature extractors in high-dimensional Bayesian optimization and emphasizes the importance of open-source collaboration in advancing research in this dynamic field.
5 snips
Aug 20, 2025 • 1h 25min
#139 Efficient Bayesian Optimization in PyTorch, with Max Balandat
Max Balandat, who leads the modeling and optimization team at Meta, discusses the fascinating world of Bayesian optimization and the BoTorch library. He shares insights on the seamless integration of BoTorch with PyTorch, enhancing flexibility for researchers. The conversation delves into the significance of adaptive experimentation and the impact of LLMs on optimization. Max emphasizes the importance of effectively communicating uncertainty to stakeholders and reflects on the transition from academia to industry, highlighting collaboration in research.
12 snips
Aug 13, 2025 • 21min
BITESIZE | What's Missing in Bayesian Deep Learning?
Yingzhen Li, a researcher specializing in Bayesian communication and uncertainty in neural networks, teams up with François-Xavier Briol, who focuses on machine learning tools for Bayesian statistics. They dive into the complexities of Bayesian deep learning, emphasizing uncertainty quantification and its role in effective modeling. The discussion covers the evolution of Bayesian models, simulation-based inference methods, and the urgent need for better computational tools to tackle high-dimensional challenges. Their insights on integrating machine learning with Bayesian approaches spark exciting possibilities in the field.
