#158 Bayesian Workflows & Foundation Models, with Stefan Radev

About this episode

The episode discusses Bayesian workflows and the role of generative AI in prior elicitation with guest Stefan Radev.

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 are prior predictive checks so underused in practice, and how do simulations help? A: They're underused because researchers don't always think to run them before seeing data -- but also because doing them rigorously (in the style Michael Betancourt advocates, with prior push-forward checks on interpretable summaries) takes effort. Simulations make it cheap to generate thousands of “what-if world” datasets from your model and check whether they look plausible, catching bad priors before you ever touch real data. Q: How can generative AI help with prior elicitation? A: Rather than forcing a domain expert to choose a distributional family and parameterize it, you can use a generative model to translate their qualitative knowledge directly into a prior. The expert describes what realistic data should look like; the generative model produces synthetic datasets matching that description; those datasets are used to fit a prior distribution. It removes the…

People in this episode

Host: Alexandre Andorra

Guest: Stefan Radev

Topics covered

• Bayesian workflows
• generative AI
• prior predictive checks
• foundation models
• Bayesian inference

Keywords

• Bayesian statistics
• prior predictive checks
• generative AI
• foundation models
• Bayesian inference

Mentioned in this episode

Organizations: Patreon

Books & works: Good Bayesian

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