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Variance reduction in MCMC

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Author Info
Mira Antonietta () (Department of Economics, University of Insubria, Italy)
Tenconi Paolo () (University of Switzerland)
Bressanini Dario () (University of Insubria, Italy)
Abstract

We propose a general purpose variance reduction technique for MCMC estimators. The idea is obtained by combining standard variance reduction principles known for regular Monte Carlo simulations (Ripley, 1987) and the Zero-Variance principle introduced in the physics literature (Assaraf and Caffarel, 1999). The potential of the new idea is illustrated with some toy examples and an application to Bayesian estimation

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File URL: http://eco.uninsubria.it/dipeco/Quaderni/files/QF2003_29.pdf
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Publisher Info
Paper provided by Department of Economics, University of Insubria in its series Economics and Quantitative Methods with number qf0310.

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Length: 18 pages
Date of creation: Sep 2003
Date of revision:
Handle: RePEc:ins:quaeco:qf0310

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Related research
Keywords: Markov chain Monte carlo; Metropolis-Hastings algorithm; Variance reduction; Zero-Variance principle;

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This page was last updated on 2009-11-27.

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