A kind of dual form for coupling from the past algorithm, to sample from Markov chain steady-state probability

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

Monte Carlo Methods and Applications, 2019, vol. 25, issue 4, 317-327

Abstract: The standard coupling from the past (CFTP) algorithm is an interesting tool to sample from exact Markov chain steady-state probability. The CFTP detects, with probability one, the end of the transient phase (called burn-in period) of the chain and consequently the beginning of its stationary phase. For large and/or stiff Markov chains, the burn-in period is expensive in time consumption. In this work, we propose a kind of dual form for CFTP called D-CFTP that, in many situations, reduces the Monte Carlo simulation time and does not need to store the history of the used random numbers from one iteration to another. A performance comparison of CFTP and D-CFTP will be discussed, and some numerical Monte Carlo simulations are carried out to show the smooth running of the proposed D-CFTP.

Keywords: Monte Carlo simulation; Markov chain; steady-state probability; coupling from the past; stiffness (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/mcma-2019-2050

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