BITESIZE | How Bayesian Additive Regression Trees Work in Practice
Soccer Factor Model DashboardUnveiling True Talent: The Soccer Factor Model for Skill EvaluationLBS #91, Exploring European Football Analytics, with Max GöbelGet early access to Alex's next live-cohort courses!Today’s clip is from episode 142 of the podcast, with Gabriel Stechschulte.Alex and Garbriel explore the re-implementation of BART (Bayesian Additive Regression Trees) in Rust, detailing the technical challenges and performance improvements achieved.They also share insights into the benefits of BART, such as uncertainty quantification, and its application in various data-intensive fields.Get the full discussion here.Intro to Bayes Course (first 2 lessons free)Advanced Regression 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!Visit our Patreon page to unlock exclusive Bayesian swag ;)TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Get early access to Alex's next live-cohort courses!Intro to Bayes Course (first 2 lessons free)Advanced Regression 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!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 statistics18:55 – Introduction to BART (Bayesian Additive Regression Trees)24:40 – Re-implementing BART in Rust for speed and scalability32:05 – Comparing BART with Gaussian Processes and other tree methods39:50 – Strengths and limitations of BART47:15 – Handling missing data and different likelihoods54:30 – Variational inference and big data challenges01:01:10 – Embedding BART into optimization and decision-making frameworks01:08:45 – Open source, PyMC, and community support01:15:20 – Advice for newcomers01:20:55 – Future of BART, Rust, and probabilistic programmingThank 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...
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BITESIZE | How Probability Becomes Causality?
Get early access to Alex's next live-cohort courses!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.Get the full discussion here.Intro to Bayes Course (first 2 lessons free)Advanced Regression 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!Visit our Patreon page to unlock exclusive Bayesian swag ;)TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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#141 AI Assisted Causal Inference, with Sam Witty
Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Get early access to Alex's next live-cohort courses!Enroll in the Causal AI workshop, to learn live with Alex (15% off if you're a Patron of the show)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:Causal inference is crucial for understanding the impact of interventions in various fields.ChiRho is a causal probabilistic programming language that bridges mechanistic and data-driven models.ChiRho allows for easy manipulation of causal models and counterfactual reasoning.The design of ChiRho emphasizes modularity and extensibility for diverse applications.Causal inference requires careful consideration of assumptions and model structures.Real-world applications of causal inference can lead to significant insights in science and engineering.Collaboration and communication are key in translating causal questions into actionable models.The future of causal inference lies in integrating probabilistic programming with scientific discovery.Chapters:05:53 Bridging Mechanistic and Data-Driven Models09:13 Understanding Causal Probabilistic Programming12:10 ChiRho and Its Design Principles15:03 ChiRho’s Functionality and Use Cases17:55 Counterfactual Worlds and Mediation Analysis20:47 Efficient Estimation in ChiRho24:08 Future Directions for Causal AI50:21 Understanding the Do-Operator in Causal Inference56:45 ChiRho’s Role in Causal Inference and Bayesian Modeling01:01:36 Roadmap and Future Developments for ChiRho01:05:29 Real-World Applications of Causal Probabilistic Programming01:10:51 Challenges in Causal Inference Adoption01:11:50 The Importance of Causal Claims in Research01:18:11 Bayesian Approaches to Causal Inference01:22:08 Combining Gaussian Processes with Causal Inference01:28:27 Future Directions in Probabilistic Programming and Causal InferenceThank 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...
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BITESIZE | How to Think Causally About Your Models?
Get early access to Alex's next live-cohort courses!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.Get the full discussion here.Intro to Bayes Course (first 2 lessons free)Advanced Regression 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!Visit our Patreon page to unlock exclusive Bayesian swag ;)TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
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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