{"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":"Bitesize | \"What Would Have Happened?\" - Bayesian Synthetic Control Explained","slug":"bitesize-what-would-have-happened-bayesian-synthetic-control-explained","published_at":"2026-04-02T18:00:00+00:00","page_url":"https://stenobird.com/podcast/learning-bayesian-statistics/bitesize-what-would-have-happened-bayesian-synthetic-control-explained","show_page_url":"https://stenobird.com/podcast/learning-bayesian-statistics","url":"https://api.riverside.com/hosting-analytics/media/860bd554239d349e6536ee0ed1f424b081a36d62cb48de9cfccb10d3cbab8b48/eyJlcGlzb2RlSWQiOiJjYjIyZTE0Ni1iY2M2LTQ1OWEtYTNiZS1iNDQzY2VlNDE2YTEiLCJwb2RjYXN0SWQiOiI3OWUwYTRmYi05N2FiLTRlOTUtYTg3NS0yNGE4YjllZTI3ZGEiLCJhY2NvdW50SWQiOiI2NDQ4M2JiZWM3ZjQ1MTFhYThjMzE1Y2QiLCJwYXRoIjoibWVkaWEvY2xpcHMvNjljZWE1Y2NjMDUwOTJiYTU4MWFiYTI2L2FsZXhhbmRyZS1hbmRvcnJhcy1zdHVkaW8tY29tcG9zZXItMjAyNi00LTJfXzE5LTIyLTIwLm1wMyJ9.mp3","audio_url":"https://api.riverside.com/hosting-analytics/media/860bd554239d349e6536ee0ed1f424b081a36d62cb48de9cfccb10d3cbab8b48/eyJlcGlzb2RlSWQiOiJjYjIyZTE0Ni1iY2M2LTQ1OWEtYTNiZS1iNDQzY2VlNDE2YTEiLCJwb2RjYXN0SWQiOiI3OWUwYTRmYi05N2FiLTRlOTUtYTg3NS0yNGE4YjllZTI3ZGEiLCJhY2NvdW50SWQiOiI2NDQ4M2JiZWM3ZjQ1MTFhYThjMzE1Y2QiLCJwYXRoIjoibWVkaWEvY2xpcHMvNjljZWE1Y2NjMDUwOTJiYTU4MWFiYTI2L2FsZXhhbmRyZS1hbmRvcnJhcy1zdHVkaW8tY29tcG9zZXItMjAyNi00LTJfXzE5LTIyLTIwLm1wMyJ9.mp3","summary":"Today's clip is from Episode 154 of the podcast, with Thomas Pinder. In this conversation, Thomas Pinder explains how Bayesian methods naturally lend themselves to causal modeling, and why that matters for real-world business decisions. The key insight is that causal questions in industry are rarely black and white: instead of a single treatment effect, you get a full posterior distribution, credible intervals, and the ability to communicate the probability that an effect is positive, which is far more useful to stakeholders than a p-value. Thomas then dives into Bayesian Synthetic Control, a reframing of the classic synthetic control method from a constrained optimization problem into a Bayesian regression problem. Rather than optimizing weights on a simplex, you place a Dirichlet prior on the regression coefficients, which turns out to be not just mathematically elegant but practically richer: you can express prior beliefs about how many control units are informative, set the concentration parameter accordingly, or let a gamma hyperprior on that parameter let the data decide. The result is a more flexible, less fragile counterfactual, implemented cleanly in PyMC or NumPyro. Get the full discussion here Support & Resources → Support the show on Patreon: https://www.patreon.com/c/learnbayesstats → Bayesian Modeling Course (first 2 lessons free): https://topmate.io/alex_andorra/1011122 Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !","meta_description":"Today's clip is from Episode 154 of the podcast, with Thomas Pinder. In this conversation, Thomas Pinder explains how Bayesian methods naturally lend them…","key_points":[],"chapters":[],"topics":[],"duration_seconds":323,"processing_state":"failed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/learning-bayesian-statistics/episodes/bitesize-what-would-have-happened-bayesian-synthetic-control-explained/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/bitesize-what-would-have-happened-bayesian-synthetic-control-explained.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}