## Simulation and the Monte Carlo MethodThis accessible new edition explores the major topics in MonteCarlo simulation
The book begins with a modernized introduction that addressesthe basic concepts of probability, Markov processes, and convexoptimization. Subsequent chapters discuss the dramatic changes thathave occurred in the field of the Monte Carlo method, with coverageof many modern topics including: - Markov Chain Monte Carlo
- Variance reduction techniques such as the transform likelihoodratio method and the screening method
- The score function method for sensitivity analysis
- The stochastic approximation method and the stochasticcounter-part method for Monte Carlo optimization
- The cross-entropy method to rare events estimation andcombinatorial optimization
- Application of Monte Carlo techniques for counting problems,with an emphasis on the parametric minimum cross-entropymethod
An extensive range of exercises is provided at the end of eachchapter, with more difficult sections and exercises markedaccordingly for advanced readers. A generous sampling of appliedexamples is positioned throughout the book, emphasizing variousareas of application, and a detailed appendix presents anintroduction to exponential families, a discussion of thecomputational complexity of stochastic programming problems, andsample MATLAB programs. Requiring only a basic, introductory knowledge of probabilityand statistics, |

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### Contents

Random Number Random Variable | |

Problems | |

Simulation of DiscreteEvent Systems | |

Counting viaMonte Carlo | |

Markov ChainMonte Carlo | |

Statistical Analysisof DiscreteEvent Systems 4 1 Introduction 4 2 Static Simulation Models | |

Controlling theVariance | |

Problems | |

Sensitivity Analysisand MonteCarlo Optimization | |

The CrossEntropy Method | |

and Decision Making 9 7 Numerical Results | |

Appendix | |

Sensitivity Analysis | |

Abbreviations and Acronyms | |