Stochastic spectral methods for efficient Bayesian solution of inverse problems
- Sandia National Laboratories, Livermore, CA 94551 (United States)
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.
- OSTI ID:
- 20991586
- Journal Information:
- Journal of Computational Physics, Vol. 224, Issue 2; Other Information: DOI: 10.1016/j.jcp.2006.10.010; PII: S0021-9991(06)00483-9; Copyright (c) 2006 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved; Country of input: International Atomic Energy Agency (IAEA); ISSN 0021-9991
- Country of Publication:
- United States
- Language:
- English
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