Layered Multishift Coupling
for use in Perfect Sampling Algorithms

(with a primer on CFTP)


David Bruce Wilson

In this article we describe a new coupling technique which is useful in a variety of perfect sampling algorithms. A multishift coupler generates a random function f() so that for each real x, f(x)-x is governed by the same fixed probability distribution, such as a normal distribution. We develop the class of layered multishift couplers, which are simple and have several useful properties. For the standard normal distribution, for instance, the layered multishift coupler generates an f() which (surprisingly) maps an interval of length l to fewer than 2+l/2.35 points --- useful in applications which perform computations on each such image point. The layered multishift coupler improves and simplifies algorithms for generating perfectly random samples from several distributions, including and autogamma distribution, posterior distributions for Bayesian inference, and the steady state distribution for certain storage systems. We also use the layered multishift coupler to develop a Markov-chain based perfect sampling algorithm for the autonormal distribution.

At the request of the organizers, we begin by giving a primer on CFTP (coupling from the past); CFTP and Fill's algorithm are the two predominant techniques for generating perfectly random samples using coupled Markov chains.

Code for the autonormal. Uses read-once CFTP, the layered multishift coupler for the normal, and Murdoch's method for dealing with unbounded state spaces.
Jesper Møller's code for measuring coalescence times for the autogamma.
Modified version of Møller's code which uses the layered multishift coupler for the gamma distribution.

PostScript version

In Neil Madras, editor, Monte Carlo Methods, volume 26 of Fields Institute Communications, pages 141--176. American Mathematical Society, 2000. To appear. Copyright © 2000 American Mathematical Society.