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Bayesian Experimental Design: Principles and Computation

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RCLW03 - Accelerating statistical inference and experimental design with machine learning

Bayesian experimental design (BED) provides a powerful and principled information-theoretic framework for optimising the design of experiments. In this tutorial, we will introduce the core principles underlying BED before discussing computational methods essential for practical implementation, with a particular focus on modern approximation techniques such as (amortised) variational inference. Finally, will cover the policy-based BED (PB-BED), which enables efficient online deployment of the BED pipeline by learning adaptive design strategies through offline training.

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

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Lecture Theatre, MRC-CBU http://meet.google.com/bzk-rvoq-uys Cormack Room, University Centre, Granta Place, CB2 1RU, Mill Lane, United Kingdom

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