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41st Annual Symposium on Foundations of Computer Science
The randomness recycler: a new technique for perfect sampling
Redondo Beach, California
November 12-November 14
ISBN: 0-7695-0850-2
J.A. Fill, Dept. of Math. Sci., Johns Hopkins Univ., MD, USA
M. Huber, Dept. of Math. Sci., Johns Hopkins Univ., MD, USA
For many probability distributions of interest, it is quite difficult to obtain samples efficiently. Often, Markov chains are employed to obtain approximately random samples from these distributions. The primary drawback to traditional Markov chain methods is that the mixing time of the chain is usually unknown, which makes it impossible to determine how close the output samples are to having the target distribution. The authors present a novel protocol, the randomness recycler (RR), that overcomes this difficulty. Unlike classical Markov chain approaches, an RR-based algorithm creates samples drawn exactly from the desired distribution. Other perfect sampling methods such as coupling from the past, use existing Markov chains, but RR does not use the traditional Markov chain at all. While by no means universally useful, RR does apply to a wide variety of problems. In restricted instances of certain problems, it gives the first expected linear time algorithms for generating samples. The authors apply RR to self-organizing lists, the Ising model, random independent sets, random colorings, and the random cluster model.
Index Terms:
sampling methods; probability; self-adjusting systems; random processes; graph theory; randomness recycler; perfect sampling; probability distributions; Markov chains; approximately random samples; mixing time; output samples; target distribution; classical Markov chain approaches; RR-based algorithm; perfect sampling methods; restricted instances; first expected linear time algorithms; sample generation; self-organizing lists; Ising model; random independent sets; random colorings; random cluster model; RR protocol
J.A. Fill, M. Huber, "The randomness recycler: a new technique for perfect sampling," focs, pp.503, 41st Annual Symposium on Foundations of Computer Science, 2000
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