STOCHASTIC SIMULATION

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. The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. Given the wide range of examples, exercises and applications students, practitioners and researchers in probability, statistics, operations research, economics, finance, engineering as well as biology and chemistry and physics will find the book of value. TOC:What this Book is about.- Part A: General Methods and Algorithms.- Generating Random Objects.- Output Analysis.- Steady-State Simulation.- Variance Reduction Methods.- Rare Event Simulation.- Gradient Estimation.- Stochastic Optimization.- Part B: Algorithms for Special Models.- Numerical Integration.- Stochastic Differential Equations.- Gaussian Processes.- L?vy Processes.- Markov Chain Monte Carlo Methods.- Selected Topics and Extended Examples.- Appendix.- Bibliography.- Index.