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2006 Examples comparing importance sampling and the Metropolis algorithm
Federico Bassetti, Persi Diaconis
Illinois J. Math. 50(1-4): 67-91 (2006). DOI: 10.1215/ijm/1258059470

Abstract

Importance sampling, particularly sequential and adaptive importance sampling, have emerged as competitive simulation techniques to Markov-chain Monte-Carlo techniques. We compare importance sampling and the Metropolis algorithm as two ways of changing the output of a Markov chain to get a different stationary distribution.

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Federico Bassetti. Persi Diaconis. "Examples comparing importance sampling and the Metropolis algorithm." Illinois J. Math. 50 (1-4) 67 - 91, 2006. https://doi.org/10.1215/ijm/1258059470

Information

Published: 2006
First available in Project Euclid: 12 November 2009

zbMATH: 1102.60060
MathSciNet: MR2247824
Digital Object Identifier: 10.1215/ijm/1258059470

Subjects:
Primary: 60J10
Secondary: 65C05

Rights: Copyright © 2006 University of Illinois at Urbana-Champaign

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Vol.50 • No. 1-4 • 2006
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