{"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":"#157 Amortized Inference & BayesFlow in Practice, with Stefan Radev","slug":"157-amortized-inference-bayesflow-in-practice-with-stefan-radev","published_at":"2026-05-06T04:45:00+00:00","page_url":"https://stenobird.com/podcast/learning-bayesian-statistics/157-amortized-inference-bayesflow-in-practice-with-stefan-radev","show_page_url":"https://stenobird.com/podcast/learning-bayesian-statistics","url":"https://api.riverside.com/hosting-analytics/media/db7c89f08159ca376ac59eb2ebd92d19a417209ac2b694b4644c972638c7a00e/eyJlcGlzb2RlSWQiOiJhYzg5NzY3Mi0zYzk3LTQ0NDctYTg5ZC1iZjYzZjQwMzJkNTkiLCJwb2RjYXN0SWQiOiI3OWUwYTRmYi05N2FiLTRlOTUtYTg3NS0yNGE4YjllZTI3ZGEiLCJhY2NvdW50SWQiOiI2NDQ4M2JiZWM3ZjQ1MTFhYThjMzE1Y2QiLCJwYXRoIjoibWVkaWEvY2xpcHMvNjlmYWM0YmNjMWY1OTZmYzYyMDBjMDkxL2FsZXhhbmRyZS1hbmRvcnJhcy1zdHVkaW8tY29tcG9zZXItMjAyNi01LTZfXzYtMzQtNC5tcDMifQ==.mp3","audio_url":"https://api.riverside.com/hosting-analytics/media/db7c89f08159ca376ac59eb2ebd92d19a417209ac2b694b4644c972638c7a00e/eyJlcGlzb2RlSWQiOiJhYzg5NzY3Mi0zYzk3LTQ0NDctYTg5ZC1iZjYzZjQwMzJkNTkiLCJwb2RjYXN0SWQiOiI3OWUwYTRmYi05N2FiLTRlOTUtYTg3NS0yNGE4YjllZTI3ZGEiLCJhY2NvdW50SWQiOiI2NDQ4M2JiZWM3ZjQ1MTFhYThjMzE1Y2QiLCJwYXRoIjoibWVkaWEvY2xpcHMvNjlmYWM0YmNjMWY1OTZmYzYyMDBjMDkxL2FsZXhhbmRyZS1hbmRvcnJhcy1zdHVkaW8tY29tcG9zZXItMjAyNi01LTZfXzYtMzQtNC5tcDMifQ==.mp3","summary":"Support & Resources → Support the show on Patreon → 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: What is simulation-based inference and what does \"sim-to-real\" mean? A: Simulation-based inference (SBI) uses a mechanistic simulator as an epistemic tool: you train a neural network on a large number of labeled simulations and then deploy it on real, unlabeled data. The \"sim-to-real\" framing captures the key asymmetry -- your network never sees real data during training, only simulations, but it generalizes to real observations at inference time. This is the opposite of the more common \"synthetic-for-ML\" approach, where fake data is used purely to augment real training data. Q: What is the amortized inference agent skill and what does it do? A: It's an open-source AI agent skill, co-developed by Stefan and Alexandre, that teaches an AI coding agent to run a complete, state-of-the-art amortized inference workflow. Because amortized inference is recent enough that it's underrepresented in LLM training data, vanilla agents tend to get it wrong. The skill injects the right methodology: it guides the agent to set up the simulator, choose the right network architecture, run a pilot, train with appropriate diagnostics, and produce an actionable report -- without the user needing to know the details. Q: What is calibration coverage and why should you never skip it? A: Calibration coverage tells you whether your posterior uncertainty is honest -- whether your credible intervals actually contain the true parameter at the right frequency. A model can show poor parameter recovery yet still be well-calibrated (because it's falling back on the prior), or it…","meta_description":"Support & Resources → Support the show on Patreon → Bayesian Modeling Course (first 2 lessons free) Our theme music is « Good Bayesian », by Baba Brin…","key_points":[],"chapters":[],"topics":[],"duration_seconds":4723,"processing_state":"failed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/learning-bayesian-statistics/episodes/157-amortized-inference-bayesflow-in-practice-with-stefan-radev/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/157-amortized-inference-bayesflow-in-practice-with-stefan-radev.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}