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

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.
Authors:
 [1] ;  [2] ;  [3]
  1. Courant Institute, New York, NY (United States)
  2. Univ. of Arizona, Tucson, AZ (United States)
  3. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Grant/Contract Number:
AC02-05CH11231; DMS-1217065; DMS-1418775; DMS-1419044
Type:
Accepted Manuscript
Journal Name:
Communications on Pure and Applied Mathematics
Additional Journal Information:
Journal Volume: 69; Journal Issue: 10; Journal ID: ISSN 0010-3640
Publisher:
Wiley
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); National Science Foundation (NSF)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING
OSTI Identifier:
1418471