Variational Bayesian Optimal Experimental Design

Autor:	Foster, A, Jankowiak, M, Bingham, E, Horsfall, P, Tee, YW, Rainforth, T, Goodman, N
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	Methodology (stat.ME) FOS: Computer and information sciences Computer Science - Machine Learning Statistics - Machine Learning Machine Learning (stat.ML) Statistics - Computation Computation (stat.CO) Statistics - Methodology Machine Learning (cs.LG)
Popis:	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. 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
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f5c9710219e76900633f1fc067e5bf29 http://arxiv.org/abs/1903.05480 Zobrazit plný text záznamu