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Generating perfect samples from distributions using Markov chains has a wide range of applications, from statistical physics to approximation algorithms.

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One of the primary methods for creating perfect samples, the coupling from the past protocol of Propp and Wilson, suffers from the fact that the user must be willing to commit to running the algorithm in its entirety in order to obtain unbiased, perfect samples.By using another method, the acceptance rejection method of Fill, Murdoch, and Rosenthal (FMR), the user may abort the procedure in the middle of a run without introducing bias. Although carefully collected, accuracy cannot be guaranteed. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

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