This book illustrates the application of perfect simulation ideas and algorithms to a wide range of problems. The author describes numerous protocol methodologies for designing algorithms for specific problems. He first examines the commonly used acceptance/rejection (AR) protocol for creating perfect simulation algorithms. He then covers other major protocols, including coupling from the past (CFTP); the Fill, Machida, Murdoch, and Rosenthal (FMMR) method; the randomness recycler; retrospective sampling; and partially recursive AR, along with multiple variants of these protocols.

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.