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Bayesian Workflow

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RCLW03 - Accelerating statistical inference and experimental design with machine learning

The workflow of applied Bayesian statistics includes not just inference but also building, checking, and understanding fitted models. We discuss various live issues including prior distributions, data models, and computation, in the context of ideas such as the Fail Fast Principle and the Folk Theorem of Statistical Computing. We also consider some examples of Bayesian models that give bad answers and see if we can develop a workflow that catches such problems. For background, see here: http://www.stat.columbia.edu/~gelman/research/unpublished/Bayesian_Workflow_article.pdf

This talk is part of the Isaac Newton Institute Seminar Series series.

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Goldsmiths 1, Lecture Theatre, Department of Materials Science & Metallurgy Rooms SG1 & SG2, Alison Richard Building, 7 West Road, Cambridge, CB3 9DT convertidor

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