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Full Description
An accessible introduction to probability, stochastic processes, and statistics for computer science and engineering applications
Second edition now also available in Paperback. This updated and revised edition of the popular classic first edition relates fundamental concepts in probability and statistics to the computer sciences and engineering. The author uses Markov chains and other statistical tools to illustrate processes in reliability of computer systems and networks, fault tolerance, and performance.
This edition features an entirely new section on stochastic Petri nets—as well as new sections on system availability modeling, wireless system modeling, numerical solution techniques for Markov chains, and software reliability modeling, among other subjects. Extensive revisions take new developments in solution techniques and applications into account and bring this work totally up to date. It includes more than 200 worked examples and self-study exercises for each section.
Probability and Statistics with Reliability, Queuing and Computer Science Applications, Second Edition offers a comprehensive introduction to probability, stochastic processes, and statistics for students of computer science, electrical and computer engineering, and applied mathematics. Its wealth of practical examples and up-to-date information makes it an excellent resource for practitioners as well. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
Contents
Preface to the Paperback Edition ix
Preface to the Second Edition xi
Preface to the First Edition xiii
Acronyms xv
About the Companion Website xix
1 Introduction 1
1.1 Motivation 1
1.2 Probability Models 2
1.3 Sample Space 3
1.4 Events 6
1.5 Algebra of Events 7
1.6 Graphical Methods of Representing Events 11
1.7 Probability Axioms 13
1.8 Combinatorial Problems 19
1.9 Conditional Probability 24
1.10 Independence of Events 26
1.11 Bayes' Rule 38
1.12 Bernoulli Trials 47
2 Discrete Random Variables 65
2.1 Introduction 65
2.2 Random Variables and Their Event Spaces 66
2.3 The Probability Mass Function 68
2.4 Distribution Functions 70
2.5 Special Discrete Distributions 72
2.6 Analysis of Program MAX 97
2.7 The Probability Generating Function 101
2.8 Discrete Random Vectors 104
2.9 Independent Random Variables 110
3 Continuous Random Variables 121
3.1 Introduction 121
3.2 The Exponential Distribution 125
3.3 The Reliability and Failure Rate 130
3.4 Some Important Distributions 135
3.5 Functions of a Random Variable 154
3.6 Jointly Distributed Random Variables 159
3.7 Order Statistics 163
3.8 Distribution of Sums 174
3.9 Functions of Normal Random Variables 190
4 Expectation 201
4.1 Introduction 201
4.2 Moments 205
4.3 Expectation Based on Multiple Random Variables 209
4.4 Transform Methods 216
4.5 Moments and Transforms of Some Distributions 226
4.6 Computation of Mean Time to Failure 238
4.7 Inequalities and Limit Theorems 247
5 Conditional Distribution and Expectation 257
5.1 Introduction 257
5.2 Mixture Distributions 266
5.3 Conditional Expectation 273
5.4 Imperfect Fault Coverage and Reliability 280
5.5 Random Sums 290
6 Stochastic Processes 301
6.1 Introduction 301
6.2 Classification of Stochastic Processes 307
6.3 The Bernoulli Process 313
6.4 The Poisson Process 317
6.5 Renewal Processes 327
6.6 Availability Analysis 332
6.7 Random Incidence 342
6.8 Renewal Model of Program Behavior 346
7 Discrete-Time Markov Chains 351
7.1 Introduction 351
7.2 Computation of n-step Transition Probabilities 356
7.3 State Classification and Limiting Probabilities 362
7.4 Distribution of Times Between State Changes 371
7.5 Markov Modulated Bernoulli Process 373
7.6 Irreducible Finite Chains with Aperiodic States 376
7.7 The M/G/ 1 Queuing System 391
7.8 Discrete-Time Birth-Death Processes 400
7.9 Finite Markov Chains with Absorbing States 407
8 Continuous-Time Markov Chains 421
8.1 Introduction 421
8.2 The Birth-Death Process 428
8.3 Other Special Cases of the Birth-Death Model 465
8.4 Non-Birth-Death Processes 474
8.5 Markov Chains with Absorbing States 519
8.6 Solution Techniques 541
8.7 Automated Generation 552
9 Networks of Queues 577
9.1 Introduction 577
9.2 Open Queuing Networks 582
9.3 Closed Queuing Networks 590
9.4 General Service Distribution and Multiple Job Types 620
9.5 Non-product-form Networks 628
9.6 Computing Response Time Distribution 641
9.7 Summary 654
10 Statistical Inference 661
10.1 Introduction 661
10.2 Parameter Estimation 663
10.3 Hypothesis Testing 718
11 Regression and Analysis of Variance 753
11.1 Introduction 753
11.2 Least-squares Curve Fitting 758
11.3 The Coefficients of Determination 762
11.4 Confidence Intervals in Linear Regression 765
11.5 Trend Detection and Slope Estimation 768
11.6 Correlation Analysis 771
11.7 Simple Nonlinear Regression 774
11.8 Higher-dimensional Least-squares Fit 775
11.9 Analysis of Variance 778
A Bibliography 791
A.1 Theory 791
A.2 Applications 796
B Properties of Distributions 804
C Statistical Tables 807
D Laplace Transforms 828
E Program Performance Analysis 835
Author Index 837
Subject Index 845
