{"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":"#155 Probabilistic Programming for the Real World, with Andreas Munk","slug":"155-probabilistic-programming-for-the-real-world-with-andreas-munk","published_at":"2026-04-08T11:45:00+00:00","page_url":"https://stenobird.com/podcast/learning-bayesian-statistics/155-probabilistic-programming-for-the-real-world-with-andreas-munk","show_page_url":"https://stenobird.com/podcast/learning-bayesian-statistics","url":"https://api.riverside.com/hosting-analytics/media/958654a1fb70f5e832b5a3b257f2642206fc3ea77416db9e09eb29422674dadc/eyJlcGlzb2RlSWQiOiI5YjEzYzY4Yy1mMjhlLTRhNzktOTc1ZS03MjAzZGM0YzQ0ZDIiLCJwb2RjYXN0SWQiOiI3OWUwYTRmYi05N2FiLTRlOTUtYTg3NS0yNGE4YjllZTI3ZGEiLCJhY2NvdW50SWQiOiI2NDQ4M2JiZWM3ZjQ1MTFhYThjMzE1Y2QiLCJwYXRoIjoibWVkaWEvY2xpcHMvNjlkNjNiYmQwYzJhNzNkMzg2YzdiZmVhL2FsZXhhbmRyZS1hbmRvcnJhcy1zdHVkaW8tY29tcG9zZXItMjAyNi00LThfXzEzLTI3LTU3Lm1wMyJ9.mp3","audio_url":"https://api.riverside.com/hosting-analytics/media/958654a1fb70f5e832b5a3b257f2642206fc3ea77416db9e09eb29422674dadc/eyJlcGlzb2RlSWQiOiI5YjEzYzY4Yy1mMjhlLTRhNzktOTc1ZS03MjAzZGM0YzQ0ZDIiLCJwb2RjYXN0SWQiOiI3OWUwYTRmYi05N2FiLTRlOTUtYTg3NS0yNGE4YjllZTI3ZGEiLCJhY2NvdW50SWQiOiI2NDQ4M2JiZWM3ZjQ1MTFhYThjMzE1Y2QiLCJwYXRoIjoibWVkaWEvY2xpcHMvNjlkNjNiYmQwYzJhNzNkMzg2YzdiZmVhL2FsZXhhbmRyZS1hbmRvcnJhcy1zdHVkaW8tY29tcG9zZXItMjAyNi00LThfXzEzLTI3LTU3Lm1wMyJ9.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: Why is bridging deep learning and probabilistic programming so important? A: Deep learning is extraordinarily good at fitting complex functions, but it throws away uncertainty. Probabilistic programming keeps uncertainty explicit throughout. Combining the two – as in inference compilation – lets you get the expressiveness of neural networks while still doing proper Bayesian inference. Q: What is inference compilation and how does it relate to amortized inference? A: Amortized inference is the general idea of training a model upfront so you don't have to run expensive inference from scratch every single time. Inference compilation is a specific form of amortized inference where a neural network is trained to propose good posterior samples for a given probabilistic program – essentially learning to do inference rather than computing it fresh each query. Q: What is PyProb and what problems does it solve? A: PyProb is a probabilistic programming library designed specifically to support amortized inference workflows. It lets you write probabilistic models in Python and then train inference networks on top of them, making methods like inference compilation practical for real-world simulators and scientific models. Full takeaways here . Chapters: 00:00:00 Introduction to Bayesian Inference and Its Barriers 00:03:51 Andreas Munch's Journey into Statistics 00:10:09 Bridging the Gap: Bayesian Inference in Real-World Applications 00:15:56 Deep Learning Meets Probabilistic Programming 00:22:05 Understanding Inference Compilation and Amortized Inference 00…","meta_description":"Support & Resources → Support the show on Patreon → Bayesian Modeling Course (first 2 lessons free): Our theme music is « Good Bayesian », by Baba Bri…","key_points":[],"chapters":[],"topics":[],"duration_seconds":6847,"processing_state":"failed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/learning-bayesian-statistics/episodes/155-probabilistic-programming-for-the-real-world-with-andreas-munk/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/155-probabilistic-programming-for-the-real-world-with-andreas-munk.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}