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G-2007-36

Coupling from the Past with Randomized Quasi-Monte Carlo

and

BibTeX reference

The coupling-from-the-past (CFTP) algorithm of Propp and Wilson, also called perfect sampling, 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.

, 24 pages

Publication

Coupling from the past with randomized quasi-Monte Carlo
and
Mathematics and Computers in Simulation, 81(3), 476–489, 2010 BibTeX reference