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- W171146495 abstract "Summary Approximate sampling from combinatorially-defined sets, using the Markov chain Monte Carlo method, is discussed from the perspective of combinatorial algorithms. We also examine the associated problem of discrete integration over such sets. Recent work is reviewed, and we re-examine the underlying formal foundational framework in the light of this. We give a detailed treatment of the coupling technique, a classical method for analysing the convergence rates of Markov chains. The related topic of perfect sampling is examined: in perfect sampling, the goal is to sample exactly from the target set. We conclude with a discussion of negative results in this area: these are results which imply that there are no polynomial time algorithms of a particular type for a particular problem." @default.
- W171146495 created "2016-06-24" @default.
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- W171146495 date "1999-07-01" @default.
- W171146495 modified "2023-09-23" @default.
- W171146495 title "Random Walks on Combinatorial Objects" @default.
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- W171146495 doi "https://doi.org/10.1017/cbo9780511721335.005" @default.
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