Sheldon M. Ross, Epstein Department of Industrial and Systems Engineering, University of Southern California
EDITION STATEMENT
Edition Statement
Fifth edition
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
xii, 310 pages :
Other Physical Details
illustrations ;
Dimensions
24 cm
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index
CONTENTS NOTE
Text of Note
Machine generated contents note: Preface; Introduction; Elements of Probability; Random Numbers; Generating Discrete Random Variables; Generating Continuous Random Variables; The Discrete Event Simulation Approach; Statistical Analysis of Simulated Data; Variance Reduction Techniques; Statistical Validation Techniques; Markov Chain Monte Carlo Methods; Some Additional Topics; Exercises; References; Index
8
SUMMARY OR ABSTRACT
Text of Note
"In formulating a stochastic model to describe a real phenomenon, it used to be that one compromised between choosing a model that is a realistic replica of the actual situation and choosing one whose mathematical analysis is tractable. That is, there did not seem to be any payoff in choosing a model that faithfully conformed to the phenomenon under study if it were not possible to mathematically analyze that model. Similar considerations have led to the concentration on asymptotic or steady-state results as opposed to the more useful ones on transient time. However, the relatively recent advent of fast and inexpensive computational power has opened up another approach--namely, to try to model the phenomenon as faithfully as possible and then to rely on a simulation study to analyze it"--