Small-Noise Analysis and Symmetrization of Implicit Monte Carlo Samplers

Jonathan Goodman, Kevin K. Lin, Matthias Morzfeld

Research output: Contribution to journalArticlepeer-review


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. Computational experiments confirm the theory and show that symmetrization is effective for small noise sampling problems.

Original languageEnglish (US)
Pages (from-to)1924-1951
Number of pages28
JournalCommunications on Pure and Applied Mathematics
Issue number10
StatePublished - Oct 1 2016

ASJC Scopus subject areas

  • General Mathematics
  • Applied Mathematics


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