{"podcast":{"title":"Learning Bayesian Statistics","slug":"learning-bayesian-statistics","podcast_index_feed_id":1331380,"rss_url":"https://feeds.captivate.fm/learnbayesstats/","website_url":"https://www.learnbayesstats.com","image_url":"https://hosting-media.riverside.com/media/imports/podcasts/79e0a4fb-97ab-4e95-a875-24a8b9ee27da/2331893-1568966097324-58deab5a83dc6.jpg","author":"Alexandre Andorra","episode_count":199,"summary":"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-sour…","last_synced_at":null,"page_url":"https://stenobird.com/podcast/learning-bayesian-statistics"},"episode":{"title":"#154 Bayesian Causal Inference at Scale, with Thomas Pinder","slug":"154-bayesian-causal-inference-at-scale-with-thomas-pinder","published_at":"2026-03-25T12:31:33+00:00","page_url":"https://stenobird.com/podcast/learning-bayesian-statistics/154-bayesian-causal-inference-at-scale-with-thomas-pinder","show_page_url":"https://stenobird.com/podcast/learning-bayesian-statistics","url":"https://api.riverside.com/hosting-analytics/media/9432fde2730231f273503a54c6596478098385dc262a12de0ac994c8667b1e74/eyJlcGlzb2RlSWQiOiI0OGRlYTdkMi0xOTEzLTQ0YWUtODdlNi01NzMwOGEzY2EwMWIiLCJwb2RjYXN0SWQiOiI3OWUwYTRmYi05N2FiLTRlOTUtYTg3NS0yNGE4YjllZTI3ZGEiLCJhY2NvdW50SWQiOiI2NDQ4M2JiZWM3ZjQ1MTFhYThjMzE1Y2QiLCJwYXRoIjoibWVkaWEvY2xpcHMvNjliZWJjNmYwMDFkYzgzZmNmM2NkZjIxL2FsZXhhbmRyZS1hbmRvcnJhcy1zdHVkaW8tY29tcG9zZXItMjAyNi0zLTIxX18xNi00Mi0zOS5tcDMifQ==.mp3","audio_url":"https://api.riverside.com/hosting-analytics/media/9432fde2730231f273503a54c6596478098385dc262a12de0ac994c8667b1e74/eyJlcGlzb2RlSWQiOiI0OGRlYTdkMi0xOTEzLTQ0YWUtODdlNi01NzMwOGEzY2EwMWIiLCJwb2RjYXN0SWQiOiI3OWUwYTRmYi05N2FiLTRlOTUtYTg3NS0yNGE4YjllZTI3ZGEiLCJhY2NvdW50SWQiOiI2NDQ4M2JiZWM3ZjQ1MTFhYThjMzE1Y2QiLCJwYXRoIjoibWVkaWEvY2xpcHMvNjliZWJjNmYwMDFkYzgzZmNmM2NkZjIxL2FsZXhhbmRyZS1hbmRvcnJhcy1zdHVkaW8tY29tcG9zZXItMjAyNi0zLTIxX18xNi00Mi0zOS5tcDMifQ==.mp3","summary":"• 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: Why was GPJax created and how does it benefit researchers? A: GPJax was developed to provide a high-performance, flexible framework for Gaussian processes (GPs) within the JAX ecosystem. It allows researchers to move beyond black-box implementations and easily experiment with custom kernels and model structures while leveraging JAX’s automatic differentiation and GPU acceleration. Q: What are the primary advantages of using Gaussian processes for data modeling? A: Gaussian processes are highly effective at modeling complex, nonlinear relationships in data. Unlike many machine learning methods that only provide a point estimate, GPs offer built-in uncertainty quantification, which is essential for understanding the reliability of predictions in research and industry. Q: How does the GPJax and NumPyro integration enhance probabilistic modeling? A: The integration allows users to treat GPJax models as components within a larger NumPyro probabilistic program. This combination enables the use of advanced sampling techniques like NUTS (No-U-Turn Sampler), making it easier to build and fit complex hierarchical models that include Gaussian processes. Q: What are the main challenges when applying Gaussian processes to high-dimensional data? A: High-dimensional data significantly complicates GP modeling due to the curse of dimensionality and the cubic scaling of computational costs. In high dimensions, defining meaningful distance metrics for kernels becomes harder, often requiring specialized techniques like sparse GPs or dimensionality reduction to remain tractable. Full tak…","meta_description":"• Support & get perks ! • Bayesian Modeling course (first 2 lessons free) Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Meg…","key_points":[],"chapters":[],"topics":[],"duration_seconds":5178,"processing_state":"failed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/learning-bayesian-statistics/episodes/154-bayesian-causal-inference-at-scale-with-thomas-pinder/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/learning-bayesian-statistics/154-bayesian-causal-inference-at-scale-with-thomas-pinder.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}