NDLI: The randomness recycler: a new technique for perfect sampling

Content Provider	IEEE Xplore Digital Library
Author	Fill, J.A. Huber, M.
Copyright Year	2000
Description	Author affiliation: Dept. of Math. Sci., Johns Hopkins Univ., MD, USA (Fill, J.A.)
Abstract	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.
Starting Page	503
Ending Page	511
File Size	868839
Page Count	9
File Format	PDF
ISBN	0769508502
ISSN	02725428
DOI	10.1109/SFCS.2000.892138
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-11-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sampling methods Statistical distributions Probability distribution Protocols Clustering algorithms Monte Carlo methods Algorithm design and analysis Time measurement
Content Type	Text
Resource Type	Article

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