Gaussian Process Based Expected Information Gain Computation for Bayesian Optimal Design

Autor:	Zhihang Xu, Qifeng Liao
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	bayesian monte carlo bayesian optimal experimental design bayesian optimization Science Astrophysics QB460-466 Physics QC1-999
Zdroj:	Entropy, Vol 22, Iss 2, p 258 (2020)
Druh dokumentu:	article
ISSN:	1099-4300
DOI:	10.3390/e22020258
Popis:	Optimal experimental design (OED) is of great significance in efficient Bayesian inversion. A popular choice of OED methods is based on maximizing the expected information gain (EIG), where expensive likelihood functions are typically involved. To reduce the computational cost, in this work, a novel double-loop Bayesian Monte Carlo (DLBMC) method is developed to efficiently compute the EIG, and a Bayesian optimization (BO) strategy is proposed to obtain its maximizer only using a small number of samples. For Bayesian Monte Carlo posed on uniform and normal distributions, our analysis provides explicit expressions for the mean estimates and the bounds of their variances. The accuracy and the efficiency of our DLBMC and BO based optimal design are validated and demonstrated with numerical experiments.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/7117beabd6254ab78b934bc3b05bdbaa Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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