Lida Mavrogonatou, University of Cambridge

• Title: Optimal Bayesian experimental design for model selection through minimisation of f-divergences

• Abstract: A systematic understanding of studied phenomena has been embraced in a range of scientific disciplines where a collection of components are studied as parts of a system rather than as isolated processes. As direct observation of the studied system is often not possible, observable information is collected through experiments and subsequently used for inference of unobservable components. Given a predefined budget, Bayesian optimal experimental design methods are often employed to identify the most useful (in terms of a targeted objective) experimental conditions while accounting for potential sources of uncertainty. Unfortunately, currently adopted methods fail to address challenges arising within a modern scientific framework, due to the increased computational complexity of models that can realistically capture the studied structures. In this talk, I will present an efficient estimation framework that is shown to overcome ongoing challenges through the use of variational approximation methods. The proposed approach is applicable to optimal experimental design problems for model selection. A suitable class of metrics that are used to quantify the benefit from each experimental condition (commonly known as utility functions) is established in which the benefit is expressed as an f-divergence between predictive distributions of the competing models.