An interruptible algorithm for perfect sampling via Markov chains

An Interruptible Algorithm for Perfect Sampling via Markov Chains Short Title: Perfect Sampling via Markov Chains

For a large class of examples arising in statistical physics known as attractive spin systems (e.g., the Ising model), one seeks to sample from a probability distribution π on an enormously large state space, but elementary sampling is ruled out by the infeasibility of calculating an appropriate normalizing constant. The same difficulty arises in computer science problems where one seeks to sam...

Perfect Sampling of Harris Recurrent Markov Chains

We develop an algorithm for simulating \perfect" random samples from the invariant measure of a Harris recurrent Markov chain. The method uses backward coupling of embedded regeneration times, and works most eeectively for nite chains and for stochas-tically monotone chains even on continuous spaces, where paths may be sandwiched below \upper" and \lower" processes. Examples show that more naiv...

Perfect Sampling of Markov Chains with Piecewise Homogeneous Events

Perfect sampling is a technique that uses coupling arguments to provide a sample from the stationary distribution of a Markov chain in a finite time without ever computing the distribution. This technique is very efficient if all the events in the system have monotonicity property. However, in the general (non-monotone) case, this technique needs to consider the whole state space, which limits ...

Perfect Sampling for Nonhomogeneous Markov Chains and Hidden Markov Models By

Markov chains for sampling matchings

Markov Chain Monte Carlo algorithms are often used to sample combinatorial structures such as matchings and independent sets in graphs. A Markov chain is defined whose state space includes the desired sample space, and which has an appropriate stationary distribution. By simulating the chain for a sufficiently large number of steps, we can sample from a distribution arbitrarily close to the sta...