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Simulation 3rd Revised edition [Kõva köide]

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  • Formaat: Hardback, 274 pages, kõrgus x laius: 229x152 mm, kaal: 546 g
  • Ilmumisaeg: 01-Jan-2002
  • Kirjastus: Academic Press Inc
  • ISBN-10: 0125980531
  • ISBN-13: 9780125980531
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Simulation 3rd Revised editionSuurem pilt
  • Formaat: Hardback, 274 pages, kõrgus x laius: 229x152 mm, kaal: 546 g
  • Ilmumisaeg: 01-Jan-2002
  • Kirjastus: Academic Press Inc
  • ISBN-10: 0125980531
  • ISBN-13: 9780125980531
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780125980531.html
Märksõnad:
In the old days, says Ross (industrial engineering and operations research, U. of California-Berkeley), scientists formulating a stochastic model had to compromise between one that realistically portrayed the situation and one that could be comprehended mathematically. No more, he explains, now that fast and powerful computers are available cheap. He shows how to construct a model as faithful as possible to the phenomenon, then analyze it using a simulation study. He includes exercises for course study, but does not specify prerequisites. There's no mention of the dates of earlier editions. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Sheldon Ross' Simulation, Third 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 new edition provides a comprehensive, in-depth, and current guide for constructing probability models and simulations for a variety of purposes. It features new information, including the presentation of the Insurance Risk Model, generating a Random Vector, and evaluating an Exotic Option. Also new is coverage of the changing nature of statistical methods due to the advancements in computing technology.
Preface ix
Introduction
1(4)
Exercises
3(2)
Elements of Probability
5(32)
Sample Space and Events
5(1)
Axioms of Probability
6(1)
Conditional Probability and Independence
7(1)
Random Variables
8(2)
Expectation
10(3)
Variance
13(2)
Chebyshev's Inequality and the Laws of Large Numbers
15(2)
Some Discrete Random Variables
17(5)
Binomial Random Variables
17(1)
Poisson Random Variables
18(2)
Geometric Random Variables
20(1)
The Negative Binomial Random Variable
20(1)
Hypergeometric Random Variables
21(1)
Continuous Random Variables
22(8)
Uniformly Distributed Random Variables
22(1)
Normal Random Variables
23(2)
Exponential Random Variables
25(2)
The Poisson Process and Gamma Random Variables
27(2)
The Nonhomogeneous Poisson Process
29(1)
Conditional Expectation and Conditional Variance
30(7)
Exercises
32(4)
References
36(1)
Random Numbers
37(8)
Introduction
37(1)
Pseudorandom Number Generation
37(1)
Using Random Numbers to Evaluate Integrals
38(7)
Exercises
42(2)
References
44(1)
Generating Discrete Random Variables
45(18)
The Inverse Transform Method
45(5)
Generating a Poisson Random Variable
50(2)
Generating Binomial Random Variables
52(1)
The Acceptance-Rejection Technique
53(2)
The Composition Approach
55(1)
Generating Random Vectors
56(7)
Exercises
57(6)
Generating Continuous Random Variables
63(24)
Introduction
63(1)
The Inverse Transform Algorithm
63(4)
The Rejection Method
67(6)
The Polar Method for Generating Normal Random Variables
73(3)
Generating a Poisson Process
76(1)
Generating a Nonhomogeneous Poisson Process
77(10)
Exercises
81(4)
References
85(2)
The Discrete Event Simulation Approach
87(22)
Introduction
87(1)
Simulation via Discrete Events
87(1)
A Single-Server Queueing System
88(3)
A Queueing System with Two Servers in Series
91(2)
A Queueing System with Two Parallel Servers
93(3)
An Inventory Model
96(1)
An Insurance Risk Model
97(2)
A Repair Problem
99(3)
Exercising a Stock Option
102(1)
Verification of the Simulation Model
103(6)
Exercises
105(3)
References
108(1)
Statistical Analysis of Simulated Data
109(20)
Introduction
109(1)
The Sample Mean and Sample Variance
109(6)
Interval Estimates of a Population Mean
115(3)
The Bootstrapping Technique for Estimating Mean Square Errors
118(11)
Exercises
124(3)
References
127(2)
Variance Reduction Techniques
129(68)
Introduction
129(2)
The Use of Antithetic Variables
131(8)
The Use of Control Variates
139(8)
Variance Reduction by Conditioning
147(10)
Estimating the Expected Number of Renewals by Time t
155(2)
Stratified Sampling
157(9)
Importance Sampling
166(14)
Using Common Random Numbers
180(1)
Evaluating an Exotic Option
181(16)
Appendix: Verification of Antithetic Variable Approach When Estimating the Expected Value of Monotone Functions
185(3)
Exercises
188(7)
References
195(2)
Statistical Validation Techniques
197(26)
Introduction
197(1)
Goodness of Fit Tests
197(8)
The Chi-Square Goodness of Fit Test for Discrete Data
198(2)
The Kolmogorov-Smirnov Test for Continuous Data
200(5)
Goodness of Fit Tests When Some Parameters Are Unspecified
205(3)
The Discrete Data Case
205(3)
The Continuous Data Case
208(1)
The Two-Sample Problem
208(7)
Validating the Assumption of a Nonhomogeneous Poisson Process
215(8)
Exercises
219(2)
References
221(2)
Markov Chain Monte Carlo Methods
223(28)
Introduction
223(1)
Markov Chains
223(3)
The Hastings-Metropolis Algorithm
226(2)
The Gibbs Sampler
228(11)
Simulated Annealing
239(3)
The Sampling Importance Resampling Algorithm
242(9)
Exercises
246(3)
References
249(2)
Some Additional Topics
251(21)
Introduction
251(1)
The Alias Method for Generating Discrete Random Variables
251(4)
Simulating a Two-Dimensional Poisson Process
255(3)
Simulation Applications of an Identity for Sums of Bernoulli Random Variables
258(4)
Estimating the Distribution and the Mean of the First Passage Time of a Markov Chain
262(5)
Coupling from the Past
267(5)
Exercises
269(2)
References
271(1)
Index 272


Sheldon M. Ross is a professor in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. He received his Ph.D. in statistics at Stanford University in 1968 and has been at Berkeley ever since. 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, and a recipient of the Humboldt US Senior Scientist Award.