Exact sampling, specifically coupling from the past (CFTP), allows users to sample exactly from the stationary distribution of a Markov chain. During its nearly 20 years of existence, exact sampling has evolved into perfect simulation, which enables high-dimensional simulation from interacting distributions.Perfect Simulation illustrates the applic

Mark L. Huber is the Fletcher Jones Associate Professor of Mathematics and Statistics and George R. Roberts Fellow at Claremont McKenna College. Dr. Huber works in the area of computational probability, designing Monte Carlo methods for applications in statistics and computer science. His research interests include applied mathematics, calculus, computers, probability, and statistics. He earned a PhD from Cornell University.

Introduction. Acceptance/Rejection. Coupling from the Past. Bounding Chains. Advanced Techniques Using Coalescence. Coalescence on Continuous and Unbounded State Spaces. Spatial Point Processes. The Randomness Recycler. Advanced Acceptance/Rejection. Stochastic Differential Equations. Applications and Limitations of Perfect Simulation.