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Publications [#29377] of Mark Huber

Papers Published

  1. Mark Huber, Perfect sampling using bounding chains, The Annals of Applied Probability, vol. 14 no. 2 (August, 2004), pp. 734--753, Institute of Mathematical Statistics
    (last updated on 2007/08/05)

    Bounding chains are a technique that offers three benefits to Markov chain practitioners: a theoretical bound on the mixing time of the chain under restricted conditions, experimental bounds on the mixing time of the chain that are provably accurate, and construction of perfect sampling algorithms when used in conjunction with protocols such as coupling from the past. Perfect sampling algorithms generate variates exactly from the target distribution without the need to know the mixing time of a Markov chain at all.
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