Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Implicit stochastic models, where the data-generation distribution is intractable but sampling is possible, are ubiquitous in the natural sciences. The models typically have free parameters that need to be inferred from data collected in scientific experiments. A fundamental question is how to design the experiments so that the collected data are most useful. The field of Bayesian experimental design advocates that, ideally, we should choose designs that maximise the mutual information (MI) between the data and the parameters. For implicit models, however, this approach is severely hampered by the high computational cost of computing posteriors and maximising MI, in particular when we have more than a handful of design variables to optimise. In this paper, we propose a new approach to Bayesian experimental design for implicit models that leverages recent advances in neural MI estimation to deal with these issues. We show that training a neural network to maximise a lower bound on MI allows us to jointly determine the optimal design and the posterior. Simulation studies illustrate that this gracefully extends Bayesian experimental design for implicit models to higher design dimensions.
Original languageEnglish
Title of host publicationProceedings of the 37th International Conference on Machine Learning (ICML) 2020
PublisherPMLR
Pages5316-5326
Number of pages11
Publication statusPublished - 18 Jul 2020
EventThirty-seventh International Conference on Machine Learning 2020 - Virtual conference
Duration: 13 Jul 202018 Jul 2020
https://icml.cc/

Publication series

NameProceedings of Machine Learning Research
Volume119
ISSN (Electronic)2640-3498

Conference

ConferenceThirty-seventh International Conference on Machine Learning 2020
Abbreviated titleICML 2020
CityVirtual conference
Period13/07/2018/07/20
Internet address

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