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

About this episode

The episode discusses Bayesian experimental design and its applications in data collection and uncertainty reduction with guest Adam Foster.

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…

People in this episode

Host: Alexandre Andorra

Guest: Adam Foster

Topics covered

• Bayesian experimental design
• active learning
• data collection
• uncertainty reduction
• expected information gain

Keywords

• Bayesian modeling
• experimental design
• data collection
• uncertainty
• information gain

Mentioned in this episode

Books & works: Good Bayesian

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