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. By day, I'm a Senior data scientist. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. I also love 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 unlock exclusive Bayesian swag on Patreon!

  1. #153 The Neuroscience of Philanthropy, with Cherian Koshy

    1D AGO

    #153 The Neuroscience of Philanthropy, with Cherian Koshy

    • Support & get perks! • Bayesian Modeling 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 ! Takeaways: Q: Is generosity a natural human trait? A: Yes, generosity is hardwired in our brains and is essential for social interaction. Q: Why do people say they care about causes but not act on it? A: There is often a disconnect between stated care for causes and actual action. Understanding the conditions under which generosity aligns with a person's identity is crucial for bridging this gap. Q: How should fundraising efforts be approached? A: Fundraising should primarily focus on belief updating rather than mere persuasion. Q: What are the benefits of being generous? A: Generosity has significant mental and physical health benefits, as the brain's reward systems activate when we give, making us feel good. Q: How do our beliefs relate to our actions? A: Our beliefs about ourselves strongly influence our actions and decisions, including our decision to be generous. Q: Can generosity impact a community? A: Yes, generosity can be a powerful tool for improving community dynamics. Q: How can technology like AI assist institutions with donors? A: AI could help institutions remember donors better, improving the donor-institution relationship. Chapters:00:00 What's the role of Behavioral Science inPhilanthropy 19:57 What is The Neuroscience of Generosity? 24:40 How can we best understand Donor Decision-Making? 32:14 How can we achieve reframe Beliefs and Actions? 35:39 What is the role of Identity in Habit Formation? 38:06 What is the Generosity Gap in Philanthropy? 45:06 How can we reduce Friction in Donation Processes? 48:27 What is the role of AI and Trust in Nonprofits? 52:11 How can we build Predictive Models for Donor Behavior? 55:41 What is the role of Empathy in Sales and Stakeholder Engagement? 01:00:46 How can we best align ideas with Stakeholder Beliefs? 01:02:06 How can we explore Generosity and Memory? Thank you to my Patrons for making this episode possible! Links from the show:Come meet Alex at the Field of Play Conference in Manchester, UK, March 27, 2026! https://www.fieldofplay.co.uk/Bayesian workflow agent skillNeurogiving, The Science of Donor Decision-MakingCherian's websiteCherian's press kitLBS #89 Unlocking the Science of Exercise, Nutrition & Weight Management, with Eric Trexler

    1h 9m
  2. #152 A Bayesian decision theory workflow, with Daniel Saunders

    FEB 26

    #152 A Bayesian decision theory workflow, with Daniel Saunders

    • Support & get perks! • Proudly sponsored by PyMC Labs! Get in touch at alex.andorra@pymc-labs.com • Intro to Bayes and Advanced Regression courses (first 2 lessons free) Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Chapters:00:00 The Importance of Decision-Making in Data Science 06:41 From Philosophy to Bayesian Statistics 14:57 The Role of Soft Skills in Data Science 18:19 Understanding Decision Theory Workflows 22:43 Shifting Focus from Accuracy to Business Value 26:23 Leveraging PyTensor for Optimization 34:27 Applying Optimal Decision-Making in Industry 40:06 Understanding Utility Functions in Regulation 41:35 Introduction to Obeisance Decision Theory Workflow 42:33 Exploring Price Elasticity and Demand 45:54 Optimizing Profit through Bayesian Models 51:12 Risk Aversion and Utility Functions 57:18 Advanced Risk Management Techniques 01:01:08 Practical Applications of Bayesian Decision-Making 01:06:54 Future Directions in Bayesian Inference 01:10:16 The Quest for Better Inference Algorithms 01:15:01 Dinner with a Polymath: Herbert Simon Thank you to my Patrons for making this episode possible! Links from the show:Come meet Alex at the Field of Play Conference in Manchester, UK, March 27, 2026! https://www.fieldofplay.co.uk/A Bayesian decision theory workflowDaniel's website, LinkedIn and GitHubLBS #124 State Space Models & Structural Time Series, with Jesse GrabowskiLBS #123 BART & The Future of Bayesian Tools, with Osvaldo MartinLBS #74 Optimizing NUTS and Developing the ZeroSumNormal Distribution, with Adrian SeyboldtLBS #76 The Past, Present & Future of Stan, with Bob Carpenter

    1h 19m
  3. 151 Diffusion Models in Python, a Live Demo with Jonas Arruda

    FEB 12

    151 Diffusion Models in Python, a Live Demo with Jonas Arruda

    • Support & get perks! • Proudly sponsored by PyMC Labs! Get in touch at alex.andorra@pymc-labs.com • Intro to Bayes and Advanced Regression courses (first 2 lessons free) Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Chapters: 00:00 Exploring Generative AI and Scientific Modeling 10:27 Understanding Simulation-Based Inference (SBI) and Its Applications 15:59 Diffusion Models in Simulation-Based Inference 19:22 Live Coding Session: Implementing Baseflow for SBI 34:39 Analyzing Results and Diagnostics in Simulation-Based Inference 46:18 Hierarchical Models and Amortized Bayesian Inference 48:14 Understanding Simulation-Based Inference (SBI) and Its Importance 49:14 Diving into Diffusion Models: Basics and Mechanisms 50:38 Forward and Backward Processes in Diffusion Models 53:03 Learning the Score: Training Diffusion Models 54:57 Inference with Diffusion Models: The Reverse Process 57:36 Exploring Variants: Flow Matching and Consistency Models 01:01:43 Benchmarking Different Models for Simulation-Based Inference 01:06:41 Hierarchical Models and Their Applications in Inference 01:14:25 Intervening in the Inference Process: Adding Constraints 01:25:35 Summary of Key Concepts and Future Directions Thank you to my Patrons for making this episode possible! Links from the show: - Come meet Alex at the Field of Play Conference in Manchester, UK, March 27, 2026! - Jonas's Diffusion for SBI Tutorial & Review (Paper & Code) - The BayesFlow Library - Jonas on LinkedIn - Jonas on GitHub - Further reading for more mathematical details: Holderrieth & Erives - 150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik - 107 Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt

    1h 36m
  4. #150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik

    JAN 28

    #150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik

    • Support & get perks! • Proudly sponsored by PyMC Labs! Get in touch at alex.andorra@pymc-labs.com • Intro to Bayes and Advanced Regression courses (first 2 lessons free) Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Chapters: 00:00 Scaling Bayesian Neural Networks 04:26 Origin Stories of the Researchers 09:46 Research Themes in Bayesian Neural Networks 12:05 Making Bayesian Neural Networks Fast 16:19 Microcanonical Langevin Sampler Explained 22:57 Bottlenecks in Scaling Bayesian Neural Networks 29:09 Practical Tools for Bayesian Neural Networks 36:48 Trade-offs in Computational Efficiency and Posterior Fidelity 40:13 Exploring High Dimensional Gaussians 43:03 Practical Applications of Bayesian Deep Ensembles 45:20 Comparing Bayesian Neural Networks with Standard Approaches 50:03 Identifying Real-World Applications for Bayesian Methods 57:44 Future of Bayesian Deep Learning at Scale 01:05:56 The Evolution of Bayesian Inference Packages 01:10:39 Vision for the Future of Bayesian Statistics Thank you to my Patrons for making this episode possible! Come meet Alex at the Field of Play Conference in Manchester, UK, March 27, 2026! Links from the show: David Rügamer: * Website * Google Scholar * GitHub Emanuel Sommer: * Website * GitHub * Google Scholar Jakob Robnik: * Google Scholar * GitHub * Microcanonical Langevin paper * LinkedIn

    1h 20m

Trailers

Ratings & Reviews

4.7
out of 5
66 Ratings

About

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. By day, I'm a Senior data scientist. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. I also love 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 unlock exclusive Bayesian swag on Patreon!

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