# #156 Bayesian Experimental Design & Active Learning, with Adam Foster

Page: https://stenobird.com/podcast/learning-bayesian-statistics/156-bayesian-experimental-design-active-learning-with-adam-foster
Text version: https://stenobird.com/podcast/learning-bayesian-statistics/156-bayesian-experimental-design-active-learning-with-adam-foster.md
Podcast: [Learning Bayesian Statistics](https://stenobird.com/podcast/learning-bayesian-statistics)
Published: 2026-04-25T19:15:00+00:00
Episode link: https://api.riverside.com/hosting-analytics/media/f0f3ae72124e8be6ef50abae375a57eddbcd051e84f950d0bd26cb464812731b/eyJlcGlzb2RlSWQiOiJhNTEwYmExNC0zZTYwLTQwZDMtYTE3Ni0wOTAzNjA2YTNiMjUiLCJwb2RjYXN0SWQiOiI3OWUwYTRmYi05N2FiLTRlOTUtYTg3NS0yNGE4YjllZTI3ZGEiLCJhY2NvdW50SWQiOiI2NDQ4M2JiZWM3ZjQ1MTFhYThjMzE1Y2QiLCJwYXRoIjoibWVkaWEvY2xpcHMvNjllZDExNmRmZGNjOGYwMGUwNGVkMWI3L2FsZXhhbmRyZS1hbmRvcnJhcy1zdHVkaW8tY29tcG9zZXItMjAyNi00LTI1X18yMS05LTMzLm1wMyJ9.mp3
Audio file: https://api.riverside.com/hosting-analytics/media/f0f3ae72124e8be6ef50abae375a57eddbcd051e84f950d0bd26cb464812731b/eyJlcGlzb2RlSWQiOiJhNTEwYmExNC0zZTYwLTQwZDMtYTE3Ni0wOTAzNjA2YTNiMjUiLCJwb2RjYXN0SWQiOiI3OWUwYTRmYi05N2FiLTRlOTUtYTg3NS0yNGE4YjllZTI3ZGEiLCJhY2NvdW50SWQiOiI2NDQ4M2JiZWM3ZjQ1MTFhYThjMzE1Y2QiLCJwYXRoIjoibWVkaWEvY2xpcHMvNjllZDExNmRmZGNjOGYwMGUwNGVkMWI3L2FsZXhhbmRyZS1hbmRvcnJhcy1zdHVkaW8tY29tcG9zZXItMjAyNi00LTI1X18yMS05LTMzLm1wMyJ9.mp3
Processing state: failed
JSON: https://stenobird.com/v1/public/podcasts/learning-bayesian-statistics/episodes/156-bayesian-experimental-design-active-learning-with-adam-foster
Duration seconds: 4603

## Resource

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 Bayesian experimental design and what problem does it solve? A: It's the practice of using a Bayesian model to decide how to collect data before you collect it. Most statistical thinking starts with a fixed dataset. Bayesian experimental design sits upstream -- you have control over experimental parameters (which questions to ask, which reagents to mix, which conditions to test) and you want to choose them optimally. The Bayesian angle is to ask: what new data would most reduce my current uncertainty? Q: When should you actually use Bayesian experimental design? A: When two conditions hold: you have active control over how data is collected (not just passive observation), and you have a Bayesian model whose prior predictive distribution gives a reasonable picture of what typical data might look like. It's especially valuable when data collection is expensive or irreversible -- when the "committal step" of running an experiment has real cost, it's worth doing the analysis first. Q: What is expected information gain (EIG) and why is it central to Bayesian experimental design? A: EIG is the score you assign to a candidate experimental design -- the amount of information you expect to gain about your model parameters by running an experiment with that design. You compute it by simulating datasets from your prior predictive, doing Bayesian inference on each, and averaging how much the uncertainty decreased. What's remarkable is that you can derive the same quantity from two completely different starting points -- reducing parameter uncertaint…

## Actions

- request_transcript: `POST https://stenobird.com/v1/public/podcasts/learning-bayesian-statistics/episodes/156-bayesian-experimental-design-active-learning-with-adam-foster/transcription-requests` — Idempotently request low-priority transcript generation for this episode.
- read_markdown: `GET https://stenobird.com/podcast/learning-bayesian-statistics/156-bayesian-experimental-design-active-learning-with-adam-foster.md` — Read the agent-friendly Markdown representation of this episode resource.

A page view does not enqueue transcription. Agents should invoke `request_transcript` explicitly when they need this episode processed.

## Transcript

Full transcripts are not published on public pages unless there is a clear rights basis.