On the use of stochastic approximation Monte Carlo for Monte Carlo integration

Faming Liang

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

The stochastic approximation Monte Carlo (SAMC) algorithm has recently been proposed as a dynamic optimization algorithm in the literature. In this paper, we show in theory that the samples generated by SAMC can be used for Monte Carlo integration via a dynamically weighted estimator by calling some results from the literature of nonhomogeneous Markov chains. Our numerical results indicate that SAMC can yield significant savings over conventional Monte Carlo algorithms, such as the Metropolis-Hastings algorithm, for the problems for which the energy landscape is rugged. © 2008 Elsevier B.V. All rights reserved.
Original languageEnglish (US)
Pages (from-to)581-587
Number of pages7
JournalStatistics & Probability Letters
Volume79
Issue number5
DOIs
StatePublished - Mar 2009
Externally publishedYes

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