Widening and clustering techniques allowing the use of monotone CFTP algorithm

Bounnite Mohamed Yasser () and Nasroallah Abdelaziz ()
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Bounnite Mohamed Yasser: Cadi Ayyad University, Faculty of Sciences Semlalia, B.P. 2390, Marrakesh, Morocco
Nasroallah Abdelaziz: Cadi Ayyad University, Faculty of Sciences Semlalia, B.P. 2390, Marrakesh, Morocco

Monte Carlo Methods and Applications, 2015, vol. 21, issue 4, 301-312

Abstract: The standard Coupling From The Past (CFTP) algorithm is an interesting tool to sample from exact stationary distribution of a Markov chain. But it is very expensive in time consuming for large chains. There is a monotone version of CFTP, called MCFTP, that is less time consuming for monotone chains. In this work, we propose two techniques to get monotone chain allowing use of MCFTP: widening technique based on adding two fictitious states and clustering technique based on partitioning the state space in clusters. Usefulness and efficiency of our approaches are showed through a sample of Markov Chain Monte Carlo simulations.

Keywords: Monte Carlo simulation; Markov chain; stochastic monotonicity; coupling from the past; perfect simulation (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1515/mcma-2015-0111

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