应用随机过程（英文版•第11版） pdf epub mobi txt 电子书 下载 2026

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本书是一部经典的随机过程著作, 叙述深入浅出、涉及面广。 主要内容有随机变量、条件期望、马尔可夫链、指数分布、泊松过程、平稳过程、更新理论及排队论等，也包括了随机过程在物理、生物、运筹、网络、遗传、经济、保险、金融及可靠性中的应用。 特别是有关随机模拟的内容, 给随机系统运行的模拟计算提供了有力的工具。最新版还增加了不带左跳的随机徘徊和生灭排队模型等内容。本书约有700道习题, 其中带星号的习题还提供了解答。

本书可作为概率论与数理统计、计算机科学、保险学、物理学、社会科学、生命科学、管理科学与工程学等专业随机过程基础课教材。

Sheldon M. Ross

国际知名概率与统计学家，南加州大学工业工程与运筹系系主任。1968年博士毕业于斯坦福大学统计系，曾在加州大学伯克利分校任教多年。研究领域包括：随机模型、仿真模拟、统计分析、金融数学等。Ross教授著述颇丰，他的多种畅销数学和统计教材均产生了世界性的影响，如《概率论基础教程（第8版）》等。

1　Introduction to Probability Theory　　1
1.1　Introduction　　1
1.2　Sample Space and Events　　1
1.3　Probabilities Defined on Events　　4
1.4　Conditional Probabilities　　6
1.5　Independent Events　　9
1.6　Bayes’ Formula　　11
Exercises　　14
References　　19
2　Random Variables　　21
2.1　Random Variables　　21
2.2　Discrete Random Variables　　25
2.2.1　The Bernoulli Random Variable　　26
2.2.2　The Binomial Random Variable　　26
2.2.3　The Geometric Random Variable　　28
2.2.4　The Poisson Random Variable　　29
2.3　Continuous Random Variables　　30
2.3.1　The Uniform Random Variable　　31
2.3.2　Exponential Random Variables　　32
2.3.3　Gamma Random Variables　　33
2.3.4　Normal Random Variables　　33
2.4　Expectation of a Random Variable　　34
2.4.1　The Discrete Case　　34
2.4.2　The Continuous Case　　37
2.4.3　Expectation of a Function of a Random Variable　　38
2.5　Jointly Distributed Random Variables　　42
2.5.1　Joint Distribution Functions　　42
2.5.2　Independent Random Variables　　45
2.5.3　Covariance and Variance of Sums of Random Variables　　46
2.5.4　Joint Probability Distribution of Functions of Random Variables　　55
2.6　Moment Generating Functions　　58
2.6.1　The Joint Distribution of the Sample Mean and Sample Variance from a Normal Population　　66
2.7　The Distribution of the Number of Events that Occur 69
2.8　Limit Theorems　　71
2.9　Stochastic Processes　　77
Exercises　　79
References　　91
3　Conditional Probability and Conditional Expectation　　93
3.1　Introduction　　93
3.2　The Discrete Case 93
3.3　The Continuous Case　　97
3.4　Computing Expectations by Conditioning　　100
3.4.1　Computing Variances by Conditioning　　111
3.5　Computing Probabilities by Conditioning　　115
3.6　Some Applications　　133
3.6.1　A List Model　　133
3.6.2　A Random Graph 135
3.6.3　Uniform Priors, Polya’s Urn Model, and Bose—Einstein Statistics　　141
3.6.4　Mean Time for Patterns　　 146
3.6.5　The k-Record Values of Discrete Random Variables　　149
3.6.6　Left Skip Free Random Walks　　152
3.7　An Identity for Compound Random Variables　　157
3.7.1　Poisson Compounding Distribution　　 160
3.7.2　Binomial Compounding Distribution　　161
3.7.3　A Compounding Distribution Related to theNegative Binomial　　 162
Exercises 163
4　Markov Chains　　 183
4.1　Introduction　　183
4.2　Chapman–Kolmogorov Equations　　 187
4.3　Classification of States　　 194
4.4　Long-Run Proportions and Limiting Probabilities　　 204
4.4.1　Limiting Probabilities　　 219
4.5　Some Applications　　 220
4.5.1　The Gambler’s Ruin Problem　　220
4.5.2　A Model for Algorithmic Efficiency　　223
4.5.3　Using a Random Walk to Analyze a Probabilistic Algorithm for the Satisfiability Problem　　226
4.6　Mean Time Spent in Transient States　　231
4.7　Branching Processes　　234
4.8　Time Reversible Markov Chains　　237
4.9　Markov Chain Monte Carlo Methods　　247
4.10　Markov Decision Processes　　251
4.11　Hidden Markov Chains　　254
4.11.1　Predicting the States　　259
Exercises　　261
References　　275
5　The Exponential Distribution and the Poisson Process　　277
5.1　Introduction 277
5.2　The Exponential Distribution　　278
5.2.1　Definition　　278
5.2.2　Properties of the Exponential Distribution　　280
5.2.3　Further Properties of the Exponential Distribution　　287
5.2.4　Convolutions of Exponential Random Variables　　 293
5.3　The Poisson Process　　 297
5.3.1　Counting Processes　　 297
5.3.2　Definition of the Poisson Process　　 298
5.3.3　Interarrival and Waiting Time Distributions　　 301
5.3.4　Further Properties of Poisson Processes　　 303
5.3.5　Conditional Distribution of the Arrival Times　　 309
5.3.6　Estimating Software Reliability　　 320
5.4　Generalizations of the Poisson Process　　 322
5.4.1　Nonhomogeneous Poisson Process 322
5.4.2　Compound Poisson Process　　 327
5.4.3　Conditional or Mixed Poisson Processes　　 332
5.5　Random Intensity Functions and Hawkes Processes　　 334
Exercises　　 338
References　　 356
6　Continuous-Time Markov Chains　　 357
6.1　Introduction　　 357
6.2　Continuous-Time Markov Chains　　 358
6.3　Birth and Death Processes　　 359
6.4　The Transition Probability Function Pij(t)　　 366
6.5　Limiting Probabilities　　 374
6.6　Time Reversibility　　 380
6.7　The Reversed Chain　　 387
6.8　Uniformization　　 393
6.9　Computing the Transition Probabilities　　 396
Exercises　　 398
References　　 407
7　Renewal Theory and Its Applications　　 409
7.1　Introduction　　 409
7.2　Distribution of N(t)　　 411
7.3　Limit Theorems and Their Applications　　 415
7.4　Renewal Reward Processes　　 427
7.5　Regenerative Processes　　 436
7.5.1　Alternating Renewal Processes　　 439
7.6　Semi-Markov Processes　　 444
7.7　The Inspection Paradox　　 447
7.8　Computing the Renewal Function　　 449
7.9　Applications to Patterns　　 452
7.9.1　Patterns of Discrete Random Variables　　 453
7.9.2　The Expected Time to a Maximal Run of Distinct Values　　 459
7.9.3　Increasing Runs of Continuous Random Variables　　 461
7.10　The Insurance Ruin Problem　　 462
Exercises　　 468
References　　 479
8　Queueing Theory 481
8.1　Introduction　　 481
8.2　Preliminaries　　 482
8.2.1　Cost Equations　　 482
8.2.2　Steady-State Probabilities　　 484
8.3　Exponential Models　　 486
8.3.1　A Single-Server Exponential Queueing System　　 486
8.3.2　A Single-Server Exponential Queueing System Having Finite Capacity　　 495
8.3.3　Birth and Death Queueing Models　　 499
8.3.4　A Shoe Shine Shop　　 505
8.3.5　A Queueing System with Bulk Service　　 507
8.4　Network of Queues　　 510
8.4.1　Open Systems　　 510
8.4.2　Closed Systems　　 514
8.5　The System M / G / 1　　 520
8.5.1　Preliminaries: Work and Another Cost Identity　　 520
8.5.2　Application of Work to M/G/1　　 520
8.5.3　Busy Periods　　 522
8.6　Variations on the M / G / 1　　 523
8.6.1　The M/G/1 with Random-Sized Batch Arrivals　　 523
8.6.2　Priority Queues　　 524
8.6.3　An M/G/1 Optimization Example　　 527
8.6.4　The M/G/1 Queue with Server Breakdown　　 531
8.7　The Model G / M / 1　　 534
8.7.1　The G / M / 1 Busy and Idle Periods　　 538
8.8　A Finite Source Model　　 538
8.9　Multiserver Queues　　 542
8.9.1　Erlang’s Loss System　　 542
8.9.2　The M/M/k Queue　　 544
8.9.3　The G/M/k Queue　　 544
8.9.4　The M/G/k Queue　　 546
Exercises　　 547
References　　 558
9　Reliability Theory　　 559
9.1　Introduction　　 559
9.2　Structure Functions　　 560
9.2.　Minimal Path and Minimal Cut Sets　　 562
9.3　Reliability of Systems of Independent Components　　 565
9.4　Bounds on the Reliability Function　　 570
9.4.1　Method of Inclusion and Exclusion　　 570
9.4.2　Second Method for Obtaining Bounds on r (p)　　 578
9.5　System Life as a Function of Component Lives　　 580
9.6　Expected System Lifetime　　 587
9.6.1　An Upper Bound on the Expected Life of a Parallel System　　591
9.7　Systems with Repair 593
9.7.1　A Series Model with Suspended Animation　　597
Exercises　　599
References　　606
10　Brownian Motion and Stationary Processes　　607
10.1　Brownian Motion　　607
10.2　Hitting Times, Maximum Variable, and the Gambler’s Ruin Problem　　611
10.3　Variations on Brownian Motion　　612
10.3.1　Brownian Motion with Drift　　612
10.3.2　Geometric Brownian Motion　　612
10.4　Pricing Stock Options　　614
10.4.1　An Example in Options Pricing　　614
10.4.2　The Arbitrage Theorem　　616
10.4.3　The Black-Scholes Option Pricing Formula　　619
10.5　The Maximum of Brownian Motion with Drift　　624
10.6　White Noise　　628
10.7　Gaussian Processes　　630
10.8　Stationary and Weakly Stationary Processes　　633
10.9　Harmonic Analysis of Weakly Stationary Processes　　637
Exercises　　639
References　　644
11　Simulation　　645
11.1　Introduction　　645
11.2　General Techniques for Simulating Continuous Random Variables　　649
11.2.1　The Inverse Transformation Method　　649
11.2.2　The Rejection Method　　650
11.2.　The Hazard Rate Method　　654
11.3　Special Techniques for Simulating Continuous Random Variables　　657
11.3.1　The Normal Distribution　　657
11.3.2　The Gamma Distribution　　660
11.3.3　The Chi-Squared Distribution　　660
11.3.4　The Beta (n, m) Distribution　　661
11.3.5　The Exponential Distribution—The Von Neumann Algorithm　　662
11.4　Simulating from Discrete Distributions　　664
11.4.1　The Alias Method　　667
11.5　Stochastic Processes　　671
11.5.1　Simulating a Nonhomogeneous Poisson Process　　672
11.5.2　Simulating a Two-Dimensional Poisson Process　　677
11.6　Variance Reduction Techniques　　680
11.6.1　Use of Antithetic Variables　　681
11.6.2　Variance Reduction by Conditioning　　684
11.6.3　Control Variates　　688
11.6.4　Importance Sampling　　690
11.7　Determining the Number of Runs　　694
11.8　Generating from the Stationary Distribution of a Markov Chain　　695
11.8.1　Coupling from the Past　　695
11.8.2　Another Approach　　697
Exercises　　698
References　　705
Appendix: Solutions to Starred Exercises　　707
Index　　759
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