Acceleration of perfect sampling by skipping events

This paper presents a new method to speed up perfect sampling of Markov chains by skipping passive events during the simulation. We show that this can be done without altering the distribution of the samples. This technique is particularly efficient for the simulation of Markov chains with different time scales such as queueing networks where certain servers are much faster than others. In such cases, the coupling time of the Markov chain can be arbitrarily large while the runtime of the skipping algorithm remains bounded. This is further illustrated by several experiments that also show the role played by the entropy of the system in the performance of our algorithm.