An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions

Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes' rule, a general approach for inverse modeling pro...

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Bibliographic Details
Published in:	Journal of computational physics Vol. 294; no. C; pp. 173 - 190
Main Authors:	Li, Weixuan, Lin, Guang
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.08.2015 Elsevier
Subjects:	Adaptive algorithms Adaptive sampling Algorithms Bayesian analysis Computation Computer simulation Gaussian mixture Importance sampling Inverse Inverse modeling inverse modeling, uncertainty reduction, adaptive sampling, Gaussian mixture, mixture of polynomial chaos expansions Mixture of polynomial chaos expansions PHYSICS OF ELEMENTARY PARTICLES AND FIELDS Proposals Uncertainty reduction Uncertainty reduction Gaussian mixture Adaptive sampling Mixture of polynomial chaos expansions Inverse modeling
ISSN:	0021-9991, 1090-2716
Online Access:	Get full text
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