Variational Bayesian Optimal Experimental Design
Abstract
Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by the difficulty of obtaining accurate estimates of the expected information gain (EIG) of an experiment. To address this, we introduce several classes of fast EIG estimators by building on ideas from amortized variational inference. We show theoretically and empirically that these estimators can provide significant gains in speed and accuracy over previous approaches. We further demonstrate the practicality of our approach on a number of end-to-end experiments.
Cite
@article{arxiv.1903.05480,
title = {Variational Bayesian Optimal Experimental Design},
author = {Adam Foster and Martin Jankowiak and Eli Bingham and Paul Horsfall and Yee Whye Teh and Tom Rainforth and Noah Goodman},
journal= {arXiv preprint arXiv:1903.05480},
year = {2020}
}
Comments
Published as a conference paper at the Thirty-third Conference on Neural Information Processing Systems, Vancouver 2019. https://papers.nips.cc/paper/9553-variational-bayesian-optimal-experimental-design.pdf