Stochastic Simulation: Algorithms and Analysis

Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides a broad treatment of such sampling-based methods, as well as accompanying mathematical analysis of the convergence properties of the methods discussed. It is the first rigorous and comprehensive advanced book on stochastic simulation. The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. The first half of the book focuses on general methods; the second half discusses model-specific algorithms. A large amount of exercises and illustrations are included, making the book of value to students, practitioners and researchers in a broad range of fields.