Extension of Fill's perfect rejection sampling algorithm to general chains


by Jim Fill (Johns Hopkins), Motoya Mochida (Johns Hopkins), Duncan Murdoch (University of Western Ontario) and Jeffrey Rosenthal (University of Toronto)


By developing and applying a broad framework for rejection sampling using auxiliary randomness, we provide an extension of the perfect sampling algorithm of Fill (1998) to general chains on quite general state spaces, and describe how use of bounding processes can ease computational burden. Along the way, we unearth a simple connection between the Coupling From The Past (CFTP) algorithm originated by Propp and Wilson (1996) and our extension of Fill's algorithm.


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