Small-Noise Analysis and Symmetrization of Implicit Monte Carlo Samplers

Goodman, Jonathan; Lin, Kevin K.; Morzfeld, Matthias

doi:10.1002/cpa.21592

Title: Small-Noise Analysis and Symmetrization of Implicit Monte Carlo Samplers

Journal Article · Mon Jul 06 00:00:00 EDT 2015 · Communications on Pure and Applied Mathematics

DOI:https://doi.org/10.1002/cpa.21592· OSTI ID:1418471

Goodman, Jonathan ^[1]; Lin, Kevin K. ^[2]; Morzfeld, Matthias ^[3]

Courant Institute, New York, NY (United States)
Univ. of Arizona, Tucson, AZ (United States)
Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Implicit samplers are algorithms for producing independent, weighted samples from multivariate probability distributions. These are often applied in Bayesian data assimilation algorithms. We use Laplace asymptotic expansions to analyze two implicit samplers in the small noise regime. Our analysis suggests a symmetrization of the algorithms that leads to improved implicit sampling schemes at a relatively small additional cost. Here, computational experiments confirm the theory and show that symmetrization is effective for small noise sampling problems.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Science Foundation (NSF)

Grant/Contract Number:: AC02-05CH11231; DMS-1217065; DMS-1418775; DMS-1419044

OSTI ID:: 1418471

Journal Information:: Communications on Pure and Applied Mathematics, Vol. 69, Issue 10; ISSN 0010-3640

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 4 works

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Cited By (2)

Sampling via Measure Transport: An Introduction Marzouk, Youssef; Moselhy, Tarek; Parno, Matthew Handbook of Uncertainty Quantification https://doi.org/10.1007/978-3-319-11259-6_23-1	book	January 2016
Sampling via Measure Transport: An Introduction Marzouk, Youssef; Moselhy, Tarek; Parno, Matthew Handbook of Uncertainty Quantification https://doi.org/10.1007/978-3-319-12385-1_23	book	June 2017

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