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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Veröffentlicht in:	Journal of computational physics Jg. 294; H. C; S. 173 - 190
Hauptverfasser:	Li, Weixuan, Lin, Guang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.08.2015 Elsevier
Schlagworte:	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-Zugang:	Volltext
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