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)!

  1. Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte

    HACE 12 H

    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 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...

    1 h y 10 min
  2. AI Assisted Causal Inference, with Sam Witty

    18 SEP

    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 Models 09:13 Understanding Causal Probabilistic Programming 12:10 ChiRho and Its Design Principles 15:03 ChiRho’s Functionality and Use Cases 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...

    1 h y 38 min
  3. NFL Analytics & Teaching Bayesian Stats, with Ron Yurko

    3 SEP

    NFL Analytics & Teaching Bayesian Stats, with Ron Yurko

    Get early access to Alex's next live-cohort courses! Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! 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: 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: 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,...

    1 h y 33 min
  4. Efficient Bayesian Optimization in PyTorch, with Max Balandat

    20 AGO

    Efficient Bayesian Optimization in PyTorch, with Max Balandat

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! 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: 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 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,...

    1 h y 25 min

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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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