Perfect Simulation

<p>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.</p><p></p><p><strong>Perfect Simulation</strong> illustrates the application of perfect simulation ideas and algorithms to a wide range of problems. The book is one of the first to bring together research on simulation from statistics physics finance computer science and other areas into a unified framework. You will discover the mechanisms behind creating perfect simulation algorithms for solving an array of problems.</p><p></p><p>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 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. The book also shows how perfect simulation methods have been successfully applied to a variety of problems such as Markov random fields permutations stochastic differential equations spatial point processes Bayesian posteriors combinatorial objects and Markov processes.</p>