Coupling from the past with randomized quasi-Monte Carlo

L’Ecuyer, P. and C. Sanvido

Mathematics and Computers in Simulation (MATCOM), 2010, vol. 81, issue 3, 476-489

Abstract: The coupling-from-the-past (CFTP) algorithm of Propp and Wilson permits one to sample exactly from the stationary distribution of an ergodic Markov chain. By using it n times independently, we obtain an independent sample from that distribution. A more representative sample can be obtained by creating negative dependence between these n replicates; other authors have already proposed to do this via antithetic variates, Latin hypercube sampling, and randomized quasi-Monte Carlo (RQMC). We study a new, often more effective, way of combining CFTP with RQMC, based on the array-RQMC algorithm. We provide numerical illustrations for Markov chains with both finite and continuous state spaces, and compare with the RQMC combinations proposed earlier.

Keywords: Variance reduction; Randomized quasi-Monte Carlo; Markov chain; Perfect sampling; Coupling from the past (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:81:y:2010:i:3:p:476-489

DOI: 10.1016/j.matcom.2009.09.003

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