Atjaunināt sīkdatņu piekrišanu

Simulation 5th edition [Hardback]

4.06/5 (52 ratings by Goodreads)
(Professor, Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, USA)
  • Formāts: Hardback, 328 pages, height x width: 229x152 mm, weight: 540 g
  • Izdošanas datums: 07-Dec-2012
  • Izdevniecība: Academic Press Inc
  • ISBN-10: 0124158250
  • ISBN-13: 9780124158252
Citas grāmatas par šo tēmu:
  • Hardback
  • Cena: 105,42 €
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
Simulation 5th editionPalielināt
  • Formāts: Hardback, 328 pages, height x width: 229x152 mm, weight: 540 g
  • Izdošanas datums: 07-Dec-2012
  • Izdevniecība: Academic Press Inc
  • ISBN-10: 0124158250
  • ISBN-13: 9780124158252
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780124158252.html
Keywords:
"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"--



Ross's Simulation, Fifth Edition introduces aspiring and practicing actuaries, engineers, computer scientists and others to the practical aspects of constructing computerized simulation studies to analyze and interpret real phenomena. Readers learn to apply results of these analyses to problems in a wide variety of fields to obtain effective, accurate solutions and make predictions about future outcomes. This text explains how a computer can be used to generate random numbers, and how to use these random numbers to generate the behavior of a stochastic model over time. It presents the statistics needed to analyze simulated data as well as that needed for validating the simulation model.



New to this Edition:

  • Additional material on variance reduction, including control variables and their use in estimating the expected return at blackjack and their relation to regression analysis
  • Additional material and examples on Markov chain monte carlo methods
  • Unique material on the alias method for generating discrete random variables
  • Additional material on generating multivariate normal vectors
  • "R" software code package on the website the correlates to the specific material in the book
  • Instructors solutions manual
  • Student solutions manual


Recenzijas

"I have always liked Ross books, as he is simultaneously mathematically rigorous and very interested in applications. The biggest strength I see is the rare combination of mathematical rigor and illustration of how the mathematical methodologies are applied in practice. Books with practical perspective are rarely this rigourous and mathematically detailed. I also like the variety of exercises, which are quite challenging and demanding excellence from students." --Prof. Krzysztof Ostaszewski, Illinois State University

Papildus informācija

This reference shows the reader how a computer can be used to generate random numbers and how to use these random numbers to generate the behavior of a stochastic model over time as well as presenting the statistics needed to analyze simulated data and the simulation model
Preface ix
1 Introduction
1(4)
Exercises
3(2)
2 Elements of Probability
5(34)
2.1 Sample Space and Events
5(1)
2.2 Axioms of Probability
6(1)
2.3 Conditional Probability and Independence
7(2)
2.4 Random Variables
9(2)
2.5 Expectation
11(3)
2.6 Variance
14(2)
2.7 Chebyshev's Inequality and the Laws of Large Numbers
16(2)
2.8 Some Discrete Random Variables
18(5)
2.9 Continuous Random Variables
23(8)
2.10 Conditional Expectation and Conditional Variance
31(8)
Exercises
33(5)
Bibliography
38(1)
3 Random Numbers
39(8)
Introduction
39(1)
3.1 Pseudorandom Number Generation
39(1)
3.2 Using Random Numbers to Evaluate Integrals
40(7)
Exercises
44(1)
Bibliography
45(2)
4 Generating Discrete Random Variables
47(22)
4.1 The Inverse Transform Method
47(7)
4.2 Generating a Poisson Random Variable
54(1)
4.3 Generating Binomial Random Variables
55(1)
4.4 The Acceptance-Rejection Technique
56(2)
4.5 The Composition Approach
58(2)
4.6 The Alias Method for Generating Discrete Random Variables
60(3)
4.7 Generating Random Vectors
63(6)
Exercises
64(5)
5 Generating Continuous Random Variables
69(28)
Introduction
69(1)
5.1 The Inverse Transform Algorithm
69(4)
5.2 The Rejection Method
73(7)
5.3 The Polar Method for Generating Normal Random Variables
80(3)
5.4 Generating a Poisson Process
83(2)
5.5 Generating a Nonhomogeneous Poisson Process
85(3)
5.6 Simulating a Two-Dimensional Poisson Process
88(9)
Exercises
91(4)
Bibliography
95(2)
6 The Multivariate Normal Distribution and Copulas
97(14)
Introduction
97(1)
6.1 The Multivariate Normal
97(2)
6.2 Generating a Multivariate Normal Random Vector
99(3)
6.3 Copulas
102(5)
6.4 Generating Variables from Copula Models
107(4)
Exercises
108(3)
7 The Discrete Event Simulation Approach
111(24)
Introduction
111(1)
7.1 Simulation via Discrete Events
111(1)
7.2 A Single-Server Queueing System
112(3)
7.3 A Queueing System with Two Servers in Series
115(2)
7.4 A Queueing System with Two Parallel Servers
117(3)
7.5 An Inventory Model
120(2)
7.6 An Insurance Risk Model
122(2)
7.7 A Repair Problem
124(2)
7.8 Exercising a Stock Option
126(2)
7.9 Verification of the Simulation Model
128(7)
Exercises
129(5)
Bibliography
134(1)
8 Statistical Analysis of Simulated Data
135(18)
Introduction
135(1)
8.1 The Sample Mean and Sample Variance
135(6)
8.2 Interval Estimates of a Population Mean
141(3)
8.3 The Bootstrapping Technique for Estimating Mean Square Errors
144(9)
Exercises
150(2)
Bibliography
152(1)
9 Variance Reduction Techniques
153(80)
Introduction
153(2)
9.1 The Use of Antithetic Variables
155(7)
9.2 The Use of Control Variates
162(7)
9.3 Variance Reduction by Conditioning
169(13)
9.4 Stratified Sampling
182(10)
9.5 Applications of Stratified Sampling
192(9)
9.6 Importance Sampling
201(13)
9.7 Using Common Random Numbers
214(2)
9.8 Evaluating an Exotic Option
216(4)
9.9 Appendix: Verification of Antithetic Variable Approach When Estimating the Expected Value of Monotone Functions
220(13)
Exercises
222(9)
Bibliography
231(2)
10 Additional Variance Reduction Techniques
233(14)
Introduction
233(1)
10.1 The Conditional Bernoulli Sampling Method
233(7)
10.2 Normalized Importance Sampling
240(4)
10.3 Latin Hypercube Sampling
244(3)
Exercises
246(1)
11 Statistical Validation Techniques
247(24)
Introduction
247(1)
11.1 Goodness of Fit Tests
247(7)
11.2 Goodness of Fit Tests When Some Parameters Are Unspecified
254(3)
11.3 The Two-Sample Problem
257(6)
11.4 Validating the Assumption of a Nonhomogeneous Poisson Process
263(8)
Exercises
267(3)
Bibliography
270(1)
12 Markov Chain Monte Carlo Methods
271(30)
Introduction
271(1)
12.1 Markov Chains
271(3)
12.2 The Hastings-Metropolis Algorithm
274(2)
12.3 The Gibbs Sampler
276(11)
12.4 Continuous time Markov Chains and a Queueing Loss Model
287(3)
12.5 Simulated Annealing
290(3)
12.6 The Sampling Importance Resampling Algorithm
293(4)
12.7 Coupling from the Past
297(4)
Exercises
298(3)
Bibliography 301(2)
Index 303
Dr. Sheldon M. Ross is a professor in the Department of Industrial and Systems Engineering at the University of Southern California. He received his PhD in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction to Probability Models, Stochastic Processes, and Introductory Statistics. Professor Ross is the founding and continuing editor of the journal Probability in the Engineering and Informational Sciences. He is a Fellow of the Institute of Mathematical Statistics, a Fellow of INFORMS, and a recipient of the Humboldt US Senior Scientist Award.