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. Transforming Nutrition Science with Bayesian Methods, with Christoph Bamberg

    1天前

    Transforming Nutrition Science with Bayesian Methods, with Christoph Bamberg

    Sign up for Alex's first live cohort, about Hierarchical Model building! 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: Bayesian mindset in psychology: Why priors, model checking, and full uncertainty reporting make findings more honest and useful.Intermittent fasting & cognition: A Bayesian meta-analysis suggests effects are context- and age-dependent – and often small but meaningful.Framing matters: The way we frame dietary advice (focus, flexibility, timing) can shape adherence and perceived cognitive benefits.From cravings to choices: Appetite, craving, stress, and mood interact to influence eating and cognitive performance throughout the day.Define before you measure: Clear definitions (and DAGs to encode assumptions) reduce ambiguity and guide better study design.DAGs for causal thinking: Directed acyclic graphs help separate hypotheses from data pipelines and make causal claims auditable.Small effects, big implications: Well-estimated “small” effects can scale to public-health relevance when decisions repeat daily.Teaching by modeling: Helping students write models (not just run them) builds statistical thinking and scientific literacy.Bridging lab and life: Balancing careful experiments with real-world measurement is key to actionable health-psychology insights.Trust through transparency: Openly communicating assumptions, uncertainty, and limitations strengthens scientific credibility. Chapters: 10:35 The Struggles of Bayesian Statistics in Psychology 22:30 Exploring Appetite and Cognitive Performance 29:45 Research Methodology and Causal Inference 36:36 Understanding Cravings and Definitions 39:02 Intermittent Fasting and Cognitive Performance 42:57 Practical Recommendations for Intermittent Fasting 49:40 Balancing Experimental Psychology and Statistical Modeling 55:00 Pressing Questions in Health Psychology 01:04:50 Future Directions in Research Thank you to my Patrons for...

    1 小时 13 分钟
  2. Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte

    10月2日

    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 小时 10 分钟
  3. AI Assisted Causal Inference, with Sam Witty

    9月18日

    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 小时 38 分钟
  4. NFL Analytics & Teaching Bayesian Stats, with Ron Yurko

    9月3日

    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 小时 33 分钟

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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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