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
Text
Foster et al. "Variational Bayesian Optimal Experimental Design." Neural Information Processing Systems, 2019.Markdown
[Foster et al. "Variational Bayesian Optimal Experimental Design." Neural Information Processing Systems, 2019.](https://mlanthology.org/neurips/2019/foster2019neurips-variational/)BibTeX
@inproceedings{foster2019neurips-variational,
title = {{Variational Bayesian Optimal Experimental Design}},
author = {Foster, Adam and Jankowiak, Martin and Bingham, Elias and Horsfall, Paul and Teh, Yee Whye and Rainforth, Thomas and Goodman, Noah},
booktitle = {Neural Information Processing Systems},
year = {2019},
pages = {14036-14047},
url = {https://mlanthology.org/neurips/2019/foster2019neurips-variational/}
}