Multilevel Double Loop Monte Carlo and Stochastic Collocation Methods with Importance Sampling for Bayesian Optimal Experimental Design

An optimal experimental set-up maximizes the value of data for statistical inferences and predictions. The efficiency of strategies for finding optimal experimental set-ups is particularly important for experiments that are time-consuming or expensive to perform. For instance, in the situation when...

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Bibliographic Details
Published in:	arXiv.org
Main Authors:	Beck, Joakim, Ben Mansour Dia, Espath, Luis F R, Tempone, Raul
Format:	Paper
Language:	English
Published:	Ithaca Cornell University Library, arXiv.org 03.02.2020
Subjects:	Approximation Bayesian analysis Collocation methods Computer simulation Design of experiments Divergence Electrical impedance Fiber orientation Importance sampling Methods Monte Carlo simulation Multilevel Parameters Partial differential equations Sampling methods Sampling techniques Simulation Statistical inference
ISSN:	2331-8422
Online Access:	Get full text
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