Simulation

"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"--

Half Title; Title; Copyright; Contents; Preface; Introduction; Elements of Probability; 2.1 Sample Space and Events; 2.2 Axioms of Probability; 2.3 Conditional Probability and Independence; 2.4 Random Variables; 2.5 Expectation; 2.6 Variance; 2.7 Chebyshev's Inequality and the Laws of Large Numbers; 2.8 Some Discrete Random Variables; 2.9 Continuous Random Variables; 2.10 Conditional Expectation and Conditional Variance; Bibliography; Random Numbers; 3.1 Pseudorandom Number Generation; 3.2 Using Random Numbers to Evaluate Integrals; Bibliography; Generating Discrete Random Variables.

4.1 The Inverse Transform Method4.2 Generating a Poisson Random Variable; 4.3 Generating Binomial Random Variables; 4.4 The Acceptance4pt'0137Rejection Technique; 4.5 The Composition Approach; 4.6 The Alias Method for Generating Discrete Random Variables; 4.7 Generating Random Vectors; Generating Continuous Random Variables; 5.1 The Inverse Transform Algorithm; 5.2 The Rejection Method; 5.3 The Polar Method for Generating Normal Random Variables; 5.4 Generating a Poisson Process; 5.5 Generating a Nonhomogeneous Poisson Process; 5.6 Simulating a Two-Dimensional Poisson Process; Bibliography.

The Mult…