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Optimum Monte-Carlo sampling using Markov chains

PESKUN, P. H.·1973·Biometrika

Abstract

The sampling method proposed by Metropolis et al. (1953) requires the simulation of a Markov chain with a specified π as its stationary distribution. Hastings (1970) outlined a general procedure for constructing and simulating such a Markov chain. The matrix P of transition probabilities is constructed using a defined symmetric function sij and an arbitrary transition matrix Q. Here, for a given Q, the relative merits of the two simple choices for sij suggested by Hastings (1970) are discussed. The optimum choice for sij is shown to be one of these. For the other choice, those Q are given which are known to make the sampling method based on P asymptotically less precise than independent sampling.

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Metadata

Authors
PESKUN, P. H.
Venue
Biometrika
Published
1973
Volume
60
Issue
3
Pages
607-612
ISSN
  • 0006-3444 (ISSN-L)
  • 1464-3510
OpenAlex
W2122891730
Cited by
531
References
1
FWCI
0.61
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Optimum Monte-Carlo sampling using Markov chains. https://app.cochat.ai/papers/10.1093/biomet/60.3.607
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PESKUN, P. H. (1973). Optimum Monte-Carlo sampling using Markov chains. Biometrika. https://doi.org/10.1093/biomet/60.3.607

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