Mark Huber Publications

The Randomness Recycler: A new technique for perfect sampling
J. A. Fill, M. L. Huber, Proceedings of the 41th Annual IEEE Symposium on the Foundations of Computer Science (2001), pp. 503–511.

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 dsitributions. 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. Here we present a new protocol, the randomness recycler (RR), that overcomes this difficulty. Unlike classical Markov chain approaches, and RR-based algorithm creates samples drawn exactly 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 instaqnces of certain problems, it gives the first expected linear time algorithms for generating samples. Here we apply RR to self-organzing lists, the Ising model, random independent sets, random colorings, and the random cluster model.