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书籍说明

【书名】 A First Course in Stochastic Models
【作者】 Henk C. Tijms
【出版社】John Wiley & Sons Ltd,
【版本】
【出版日期】2003
【文件格式】PDF
【文件大小】2.76MB
【页数】430 Pages
【ISBN出版号 ISBN 0-471-49880-7 (Cloth) ISBN 0-471-49881-5 (Paper)
【资料类别】计量经济学，Time series Analysis
【市面定价】64 美元（Amazon Hardcore）
【扫描版还是影印版】
【是否缺页】完整
【关键词】1. Stochastic processes. I. Time Series Analysis
【内容简介】The teaching of applied probability needs a fresh approach. The field of applied
probability has changed profoundly in the past twenty years and yet the textbooks
in use today do not fully reflect the changes. The development of computational
methods has greatly contributed to a better understanding of the theory. It is my
conviction that theory is better understood when the algorithms that solve the
problems the theory addresses are presented at the same time. This textbook tries
to recognize what the computer can do without letting the theory be dominated
by the computational tools. In some ways, the book is a successor of my earlier
book Stochastic Modeling and Analysis. However, the set-up of the present text is
completely different. The theory has a more central place and provides a framework
in which the applications fit. Without a solid basis in theory, no applications can be
solved. The book is intended as a first introduction to stochastic models for senior
undergraduate students in computer science, engineering, statistics and operations
research, among others. Readers of this book are assumed to be familiar with the
elementary theory of probability.

【目录】

Contents
Preface ix
1 The Poisson Process and Related Processes 1
1.0 Introduction 1
1.1 The Poisson Process 1
1.1.1 The Memoryless Property 2
1.1.2 Merging and Splitting of Poisson Processes 6
1.1.3 The M/G/∞ Queue 9
1.1.4 The Poisson Process and the Uniform Distribution 15
1.2 Compound Poisson Processes 18
1.3 Non-Stationary Poisson Processes 22
1.4 Markov Modulated Batch Poisson Processes 24
Exercises 28
Bibliographic Notes 32
References 32
2 Renewal-Reward Processes 33
2.0 Introduction 33
2.1 Renewal Theory 34
2.1.1 The Renewal Function 35
2.1.2 The Excess Variable 37
2.2 Renewal-Reward Processes 39
2.3 The Formula of Little 50
2.4 Poisson Arrivals See Time Averages 53
2.5 The Pollaczek–Khintchine Formula 58
2.6 A Controlled Queue with Removable Server 66
2.7 An Up- And Downcrossing Technique 69
Exercises 71
Bibliographic Notes 78
References 78
3 Discrete-Time Markov Chains 81
3.0 Introduction 81
3.1 The Model 82

 3.2 Transient Analysis 87
3.2.1 Absorbing States 89
3.2.2 Mean First-Passage Times 92
3.2.3 Transient and Recurrent States 93
3.3 The Equilibrium Probabilities 96
3.3.1 Preliminaries 96
3.3.2 The Equilibrium Equations 98
3.3.3 The Long-run Average Reward per Time Unit 103
3.4 Computation of the Equilibrium Probabilities 106
3.4.1 Methods for a Finite-State Markov Chain 107
3.4.2 Geometric Tail Approach for an Infinite State Space 111
3.4.3 Metropolis—Hastings Algorithm 116
3.5 Theoretical Considerations 119
3.5.1 State Classification 119
3.5.2 Ergodic Theorems 126
Exercises 134
Bibliographic Notes 139
References 139
4 Continuous-Time Markov Chains 141
4.0 Introduction 141
4.1 The Model 142
4.2 The Flow Rate Equation Method 147
4.3 Ergodic Theorems 154
4.4 Markov Processes on a Semi-Infinite Strip 157
4.5 Transient State Probabilities 162
4.5.1 The Method of Linear Differential Equations 163
4.5.2 The Uniformization Method 166
4.5.3 First Passage Time Probabilities 170
4.6 Transient Distribution of Cumulative Rewards 172
4.6.1 Transient Distribution of Cumulative Sojourn Times 173
4.6.2 Transient Reward Distribution for the General Case 176
Exercises 179
Bibliographic Notes 185
References 185
5 Markov Chains and Queues 187
5.0 Introduction 187
5.1 The Erlang Delay Model 187
5.1.1 The M/M/1 Queue 188
5.1.2 The M/M/c Queue 190
5.1.3 The Output Process and Time Reversibility 192
5.2 Loss Models 194
5.2.1 The Erlang Loss Model 194
5.2.2 The Engset Model 196
5.3 Service-System Design 198
5.4 Insensitivity 202
5.4.1 A Closed Two-node Network with Blocking 203
5.4.2 The M/G/1 Queue with Processor Sharing 208
5.5 A Phase Method 209

5.6 Queueing Networks 214
5.6.1 Open Network Model 215
5.6.2 Closed Network Model 219
Exercises 224
Bibliographic Notes 230
References 231
6 Discrete-Time Markov Decision Processes 233
6.0 Introduction 233
6.1 The Model 234
6.2 The Policy-Improvement Idea 237
6.3 The Relative Value Function 243
6.4 Policy-Iteration Algorithm 247
6.5 Linear Programming Approach 252
6.6 Value-Iteration Algorithm 259
6.7 Convergence Proofs 267
Exercises 272
Bibliographic Notes 275
References 276
7 Semi-Markov Decision Processes 279
7.0 Introduction 279
7.1 The Semi-Markov Decision Model 280
7.2 Algorithms for an Optimal Policy 284
7.3 Value Iteration and Fictitious Decisions 287
7.4 Optimization of Queues 290
7.5 One-Step Policy Improvement 295
Exercises 300
Bibliographic Notes 304
References 305
8 Advanced Renewal Theory 307
8.0 Introduction 307
8.1 The Renewal Function 307
8.1.1 The Renewal Equation 308
8.1.2 Computation of the Renewal Function 310
8.2 Asymptotic Expansions 313
8.3 Alternating Renewal Processes 321
8.4 Ruin Probabilities 326
Exercises 334
Bibliographic Notes 337
References 338
9 Algorithmic Analysis of Queueing Models 339
9.0 Introduction 339
9.1 Basic Concepts

9.2 The M/G/1 Queue 345
9.2.1 The State Probabilities 346
9.2.2 The Waiting-Time Probabilities 349
9.2.3 Busy Period Analysis 353
9.2.4 Work in System 358
9.3 The MX/G/1 Queue 360
9.3.1 The State Probabilities 361
9.3.2 The Waiting-Time Probabilities 363
9.4 M/G/1 Queues with Bounded Waiting Times 366
9.4.1 The Finite-Buffer M/G/1 Queue 366
9.4.2 An M/G/1 Queue with Impatient Customers 369
9.5 The GI/G/1 Queue 371
9.5.1 Generalized Erlangian Services 371
9.5.2 Coxian-2 Services 372
9.5.3 The GI /P h/1 Queue 373
9.5.4 The Ph/G/1 Queue 374
9.5.5 Two-moment Approximations 375
9.6 Multi-Server Queues with Poisson Input 377
9.6.1 The M/D/c Queue 378
9.6.2 The M/G/c Queue 384
9.6.3 The MX/G/c Queue 392
9.7 The GI/G/c Queue 398
9.7.1 The GI/M/c Queue 400
9.7.2 The GI/D/c Queue 406
9.8 Finite-Capacity Queues 408
9.8.1 The M/G/c/c + N Queue 408
9.8.2 A Basic Relation for the Rejection Probability 410
9.8.3 The MX/G/c/c + N Queue with Batch Arrivals 413
9.8.4 Discrete-Time Queueing Systems 417
Exercises 420
Bibliographic Notes 428
References 428
Appendices 431

【书评】随机模型中的入门书