Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
522533 | Journal of Computational Physics | 2007 | 27 Pages |
Abstract
We present a reformulation of the Bayesian approach to inverse problems, that seeks to accelerate Bayesian inference by using polynomial chaos (PC) expansions to represent random variables. Evaluation of integrals over the unknown parameter space is recast, more efficiently, as Monte Carlo sampling of the random variables underlying the PC expansion. We evaluate the utility of this technique on a transient diffusion problem arising in contaminant source inversion. The accuracy of posterior estimates is examined with respect to the order of the PC representation, the choice of PC basis, and the decomposition of the support of the prior. The computational cost of the new scheme shows significant gains over direct sampling.
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Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Youssef M. Marzouk, Habib N. Najm, Larry A. Rahn,