Skip to Main Content
This course introduces the basic stochastic simulation techniques and how to use them to solve data and decision analytics problems in application domains such as manufacturing, service and finance. Students will learn the basic pipelines of simulation and also how to formulate real operation systems into suitable simulation models, and how to use computer code to implement simulation experiments. Topics include static and dynamic simulation, simulation with spreadsheets, statistical analysis of simulation results, experiment design and simulation optimization.
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.
