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A Primer on Perfect Simulation

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Statistical Physics and Spatial Statistics

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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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

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