Simulation and the Monte Carlo Method

This revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences. The book begins with a modernized intr...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Rubinstein, Reuven Y.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2016.
Colección:New York Academy of Sciences Ser.
Temas:
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • SIMULATION AND THE MONTE CARLO METHOD
  • CONTENTS
  • PREFACE
  • ACKNOWLEDGMENTS
  • CHAPTER 1 PRELIMINARIES
  • 1.1 INTRODUCTION
  • 1.2 RANDOM EXPERIMENTS
  • 1.3 CONDITIONAL PROBABILITY AND INDEPENDENCE
  • 1.4 RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS
  • 1.5 SOME IMPORTANT DISTRIBUTIONS
  • 1.6 EXPECTATION
  • 1.7 JOINT DISTRIBUTIONS
  • 1.8 FUNCTIONS OF RANDOM VARIABLES
  • 1.8.1 Linear Transformations
  • 1.8.2 General Transformations
  • 1.9 TRANSFORMS
  • 1.10 JOINTLY NORMAL RANDOM VARIABLES
  • 1.11 LIMIT THEOREMS
  • 1.12 POISSON PROCESSES
  • 1.13 MARKOV PROCESSES
  • 1.13.1 Markov Chains
  • 1.13.2 Classification of States
  • 1.13.3 Limiting Behavior
  • 1.13.4 Reversibility
  • 1.13.5 Markov Jump Processes
  • 1.14 GAUSSIAN PROCESSES
  • 1.15 INFORMATION
  • 1.15.1 Shannon Entropy
  • 1.15.2 Kullback-Leibler Cross-Entropy
  • 1.15.3 Maximum Likelihood Estimator and Score Function
  • 1.15.4 Fisher Information
  • 1.16 CONVEX OPTIMIZATION AND DUALITY
  • 1.16.1 Lagrangian Method
  • 1.16.2 Duality
  • PROBLEMS
  • REFERENCES
  • CHAPTER 2 RANDOM NUMBER, RANDOM VARIABLE, AND STOCHASTIC PROCESS GENERATION
  • 2.1 INTRODUCTION
  • 2.2 RANDOM NUMBER GENERATION
  • 2.2.1 Multiple Recursive Generators
  • 2.2.2 Modulo 2 Linear Generators
  • 2.3 RANDOM VARIABLE GENERATION
  • 2.3.1 Inverse-Transform Method
  • 2.3.2 Alias Method
  • 2.3.3 Composition Method
  • 2.3.4 Acceptance-Rejection Method
  • 2.4 GENERATING FROM COMMONLY USED DISTRIBUTIONS
  • 2.4.1 Generating Continuous Random Variables
  • 2.4.2 Generating Discrete Random Variables
  • 2.5 RANDOM VECTOR GENERATION
  • 2.5.1 Vector Acceptance-Rejection Method
  • 2.5.2 Generating Variables from a Multinormal Distribution
  • 2.5.3 Generating Uniform Random Vectors over a Simplex
  • 2.5.4 Generating Random Vectors Uniformly Distributed over a Unit Hyperball and Hypersphere
  • 2.5.5 Generating Random Vectors Uniformly Distributed inside a Hyperellipsoid
  • 2.6 GENERATING POISSON PROCESSES
  • 2.7 GENERATING MARKOV CHAINS AND MARKOV JUMP PROCESSES
  • 2.7.1 Random Walk on a Graph
  • 2.7.2 Generating Markov Jump Processes
  • 2.8 GENERATING GAUSSIAN PROCESSES
  • 2.9 GENERATING DIFFUSION PROCESSES
  • 2.10 GENERATING RANDOM PERMUTATIONS
  • PROBLEMS
  • REFERENCES
  • CHAPTER 3 SIMULATION OF DISCRETE-EVENT SYSTEMS
  • 3.1 INTRODUCTION
  • 3.2 SIMULATION MODELS
  • 3.2.1 Classification of Simulation Models
  • 3.3 SIMULATION CLOCK AND EVENT LIST FOR DEDS
  • 3.4 DISCRETE-EVENT SIMULATION
  • 3.4.1 Tandem Queue
  • 3.4.2 Repairman Problem
  • PROBLEMS
  • REFERENCES
  • CHAPTER 4 STATISTICAL ANALYSIS OF DISCRETE-EVENT SYSTEMS
  • 4.1 INTRODUCTION
  • 4.2 ESTIMATORS AND CONFIDENCE INTERVALS
  • 4.3 STATIC SIMULATION MODELS
  • 4.4 DYNAMIC SIMULATION MODELS
  • 4.4.1 Finite-Horizon Simulation
  • 4.4.2 Steady-State Simulation
  • 4.5 BOOTSTRAP METHOD
  • PROBLEMS
  • REFERENCES
  • CHAPTER 5 CONTROLLING THE VARIANCE
  • 5.1 INTRODUCTION

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