Small-Noise Analysis and Symmetrization of Implicit Monte Carlo Samplers

DOI10.1002/cpa.21592zbMath1352.65031arXiv1410.6151OpenAlexW2962828801MaRDI QIDQ2831149

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

Publication date: 2 November 2016

Published in: Communications on Pure and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1410.6151

zbMATH Keywords

algorithmnumerical exampleMarkov chain Monte Carlo methodsymmetrizationLaplace asymptotic expansionsimplicit sampler

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Sampling theory, sample surveys (62D05) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (4)

Iterative Importance Sampling Algorithms for Parameter Estimation ⋮ Importance sampling: intrinsic dimension and computational cost ⋮ Symmetrized importance samplers for stochastic differential equations ⋮ Sampling, feasibility, and priors in data assimilation

Cites Work

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• Rare Event Simulation of Small Noise Diffusions
• Blind Deconvolution via Sequential Imputations
• Stochastic Tools in Mathematics and Science
• Bayesian Analysis of DSGE Models

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