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Metropolis Methods, Gaussian Proposals and Antithetic Variables

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Book cover Stochastic Models, Statistical Methods, and Algorithms in Image Analysis

Part of the book series: Lecture Notes in Statistics ((LNS,volume 74))

Abstract

We investigate various aspects of a class of dynamic Monte Carlo methods, that generalises the Metropolis algorithm and includes the Gibbs sampler as a special case. These can be used to estimate expectations of marginal distributions in stochastic systems. A distinction is drawn between speed of weak convergence and precision of estimation. For continuously distributed processes, a particular gaussian proposal distribution is suggested: this incorporates a parameter that may be varied to improve the performance of the sampling method, by adjusting the magnitude of an “antithetic” element introduced into the sampling. The suggestion is examined in detail in some experiments based on an image analysis problem.

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Author information

Authors and Affiliations

  1. Department of Mathematics, University of Bristol, Bristol, BS8 1TW, UK

    Peter J. Green & Xiao-liang Han

Authors
  1. Peter J. Green
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  2. Xiao-liang Han
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Editor information

Editors and Affiliations

  1. Istituto per le Applicazioni del Calcolo, C.N.R., Viale del Policlinico 137, 00161, Rome, Italy

    Piero Barone  & Arnoldo Frigessi  & 

  2. Dipartimento di Matematica Pura ed Applicata, Università di L’Aquila, L’Aquila, Italy

    Mauro Piccioni

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© 1992 Springer-Verlag Berlin Heidelberg

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Green, P.J., Han, Xl. (1992). Metropolis Methods, Gaussian Proposals and Antithetic Variables. In: Barone, P., Frigessi, A., Piccioni, M. (eds) Stochastic Models, Statistical Methods, and Algorithms in Image Analysis. Lecture Notes in Statistics, vol 74. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2920-9_10

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  • DOI: https://doi.org/10.1007/978-1-4612-2920-9_10

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-97810-9

  • Online ISBN: 978-1-4612-2920-9

  • eBook Packages: Springer Book Archive

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