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Statistical Physics and Spatial Statistics pp 349–378Cite as

A Primer on Perfect Simulation

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Part of the book series: Lecture Notes in Physics ((LNP,volume 554))

Abstract

Markov Chain Monte Carlo has long become a very useful, established tool in statistical physics and spatial statistics. Recent years have seen the development of a new and exciting generation of Markov Chain Monte Carlo methods: perfect simulation algorithms. In contrast to conventional Markov Chain Monte Carlo, perfect simulation produces samples which are guaranteed to have the exact equilibrium distribution. In the following we provide an example-based introduction into perfect simulation focussed on the method called Coupling From The Past.

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Author information

Authors and Affiliations

  1. Mathematical Statistics, Chalmers University of Technology, USA

    Elke Thönnes

Authors
  1. Elke Thönnes
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Editor information

Editors and Affiliations

  1. Fachbereich Physik, Bergische Universität Wuppertal, 42097, Wuppertal, Germany

    Klaus R. Mecke

  2. Institut für Stochastik, TU Bergakademie Freiberg, 09596, Freiberg, Germany

    Dietrich Stoyan

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© 2000 Springer-Verlag Berlin Heidelberg

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Thönnes, E. (2000). A Primer on Perfect Simulation. In: Mecke, K.R., Stoyan, D. (eds) Statistical Physics and Spatial Statistics. Lecture Notes in Physics, vol 554. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45043-2_13

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  • DOI: https://doi.org/10.1007/3-540-45043-2_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67750-5

  • Online ISBN: 978-3-540-45043-6

  • eBook Packages: Springer Book Archive

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