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

MARC

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100 1 |a Rubinstein, Reuven Y. 
245 1 0 |a Simulation and the Monte Carlo Method  |h [electronic resource]. 
260 |a Newark :  |b John Wiley & Sons, Incorporated,  |c 2016. 
300 |a 1 online resource (435 p.). 
490 1 |a New York Academy of Sciences Ser. ;  |v v.10 
500 |a Description based upon print version of record. 
505 0 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
500 |a 5.2 COMMON AND ANTITHETIC RANDOM VARIABLES 
520 |a 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 introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including: Markov Chain Monte Carlo, variance reduction techniques such as importance (re-)sampling, and the transform likelihood ratio method, the score function method for sensitivity analysis, the stochastic approximation method and the stochastic counter-part method for Monte Carlo optimization, the cross-entropy method for rare events estimation and combinatorial optimization, and application of Monte Carlo techniques for counting problems. An extensive range of exercises is provided at the end of each chapter, as well as a generous sampling of applied examples. The Third Edition features a new chapter on the highly versatile splitting method, with applications to rare-event estimation, counting, sampling, and optimization. A second new chapter introduces the stochastic enumeration method, which is a new fast sequential Monte Carlo method for tree search.--Provided by publisher. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
655 0 |a Electronic books. 
758 |i has work:  |a Simulation and the Monte Carlo method (Text)  |1 https://id.oclc.org/worldcat/entity/E39PCGHKjdkXJhWgJHXP4HCc8y  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |a Rubinstein, Reuven Y.  |t Simulation and the Monte Carlo Method  |d Newark : John Wiley & Sons, Incorporated,c2016  |z 9781118632161 
830 0 |a New York Academy of Sciences Ser. 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=7103901  |z Texto completo 
938 |a ProQuest Ebook Central  |b EBLB  |n EBL7103901 
994 |a 92  |b IZTAP 

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