《應用隨機過程：概率模型導論（英文版·第10版）》敘述深入淺出，涉及面廣。主要內容有隨機變量、條件概率及條件期望、離散及連續(xù)馬爾可夫鏈、指數(shù)分布、泊松過程、布朗運動及平穩(wěn)過程、更新理論及排隊論等；也包括了隨機過程在物理、生物、運籌、網絡、遺傳、經濟、保險、金融及可靠性中的應用。特別是有關隨機模擬的內容，給隨機系統(tǒng)運行的模擬計算提供了有力的工具。除正文外，《應用隨機過程：概率模型導論（英文版·第10版）》有約700道習題，其中帶星號的習題還提供了解答。
《應用隨機過程：概率模型導論（英文版·第10版）》可作為概率論與統(tǒng)計、計算機科學、保險學、物理學、社會科學、生命科學、管理科學與工程學等專業(yè)的隨機過程基礎課教材。
《應用隨機過程：概率模型導論（英文版·第10版）》是國際知名統(tǒng)計學家Sheldon M，Ross所著的關于基礎概率理論和隨機過程的經典教材。被加州大學伯克利分校，哥倫比亞大學、普度大學、密歇根大學、俄勒岡州立大學，華盛頓大學等眾多國外知名大學所采用。 與其他隨機過程教材相比。《應用隨機過程：概率模型導論（英文版·第10版）》非常強調實踐性。內含極其豐富的例子和習題，涵蓋了眾多學科的各種應用。作者富于啟發(fā)而又不失嚴密性的敘述方式，有助于使讀者建立概率思維方式，培養(yǎng)對概率理論、隨機過程的直觀感覺。對那些需要將概率理論應用于精算學，運籌學，物理學，工程學，計算機科學。管理學和社會科學的讀者而言，《應用隨機過程：概率模型導論（英文版·第10版）》是一本極好的教材或參考書。 《應用隨機過程：概率模型導論（英文版·第10版）》特色 秉承作者招牌式的深入淺出，娓娓道來的寫作風格。 增加了關于不帶左跳的隨機徘徊、生滅排隊模型、馬爾可夫鏈和保險破產模型等方面的重要內容。 增加了新的例子和習題，更加注重強化讀者的概率直觀。
Sheldon M．Ross，國際知名概率與統(tǒng)計學家，南加州大學工業(yè)工程與運籌系系主任。1968年博士畢業(yè)于斯坦福大學統(tǒng)計系，曾在加州大學伯克利分校任教多年。研究領域包括：隨機模型、仿真模擬、統(tǒng)計分析、金融數(shù)學等。Ross教授著述頗豐，他的多種暢銷數(shù)學和統(tǒng)計教材均產生了世界性的影響，如A First Course in Probability（《概率論基礎教程》）和Simulation（《統(tǒng)計模擬》）等（均由人民郵電出版社引進出版）。
1 introduction to probability theory
1.1 introduction
1.2 sample space and events
1.3 probabilities defined on events
1.4 conditional probabilities
1.5 independent events
1.6 bayes' formula
exercises
references
2 random variables
2.1 random variables
2.2 discrete random variables
2.2.1 the bernoulli random variable
2.2.2 the binomial random variable
2.2.3 the geometric random variable
2.2.4 the poisson random variable
2.3 continuous random variables
2.3.1 the uniform random variable
2.3.2 exponential random variables
2.3.3 gamma random variables
2.3.4 normal random variables
2.4 expectation of a random variable
2.4.1 the discrete case
2.4.2 the continuous case
2.4.3 expectation of a function of a random variable
2.5 jointly distributed random variables
2.5.1 joint distribution functions
2.5.2 independent random variables
2.5.3 covariance and variance of sums of random variables
2.5.4 joint probability distribution of functions of randomvariables
2.6 moment generating functions
2.6.1 the joint distribution of the sample mean and sample variance from a normal population
2.7 the distribution of the number of events that occur
2.8 limit theorems
2.9 stochastic processes
exercises
references
3 conditional probability and conditional expectation
3.1 introduction
3.2 the discrete case
3.3 the continuous case
3.4 computing expectations by conditioning
3.4.1 computing variances by conditioning
3.5 computing probabilities by conditioning
3.6 some applications
3.6.1 a list model
3.6.2 a random graph
3.6.3 uniform priors, polya's urn model, and bose-einstein statistics
3.6.4 mean time for patterns
3.6.5 the k-record values of discrete random variables
3.6.6 left skip free random walks
3.7 an identity for compound random variables
3.7.1 poisson compounding distribution
3.7.2 binomial compounding distribution
3.7.3 a compounding distribution related to the negative binomial
exercises
4 markov chains
4.1 introduction
4.2 chapman-kolmogorov equations
4.3 classification of states
4.4 limiting probabilities
4.5 some applications
4.5.1 the gambler's ruin problem
4.5.2 a model for algorithmic efficiency
4.5.3 using a random walk to analyze a probabilistic algorithm for the satisfiability problem
4.6 mean time spent in transient states
4.7 branching processes
4.8 time reversible markov chains
4.9 markov chain monte carlo methods
4.10 markov decision processes
4.11 hidden markov chains
4.11.1 predicting the states
exercises
references
5 the exponential distribution and the poisson process
5.1 introduction
5.2 the exponential distribution
5.2.1 definition
5.2.2 properties of the exponential distribution
5.2.3 further properties of the exponential distribution
5.2.4 convolutions of exponential random variables
5.3 the poisson process
5.3.1 counting processes
5.3.2 definition of the poisson process
5.3.3 interarrival and waiting time distributions
5.3.4 further properties of poisson processes
5.3.5 conditional distribution of the arrival times
5.3.6 estimating software reliability
5.4 generalizations of the poisson process
5.4.1 nonhomogeneous poisson process
5.4.2 compound poisson process
5.4.3 conditional or mixed poisson processes
exercises
references
6 continuous-time markov chains
6.1 introduction
6.2 continuous-time markov chains
6.3 birth and death processes
6.4 the transition probability function pij (t)
6.5 limiting probabilities
6.6 time reversibility
6.7 uniformization
6.8 computing the transition probabilities
exercises
references
7 renewal theory and its applications
7.1 introduction
7.2 distribution of n(t)
7.3 limit theorems and their applications
7.4 renewal reward processes
7.5 regenerative processes
7.5.1 alternating renewal processes
7.6 semi-markov processes
7.7 the inspection paradox
7.8 computing the renewal function
7.9 applications to patterns
7.9.1 patterns of discrete random variables
7.9.2 the expected time to a maximal run of distinct values
7.9.3 increasing runs of continuous random variables
7.10 the insurance ruin problem
exercises
references
8 queueing theory
8.1 introduction
8.2 preliminaries
8.2.1 cost equations
8.2.2 steady-state probabilities
8.3 exponential models
8.3.1 a single-server exponential queueing system
8.3.2 a single-server exponential queueing system having finite capacity
8.3.3 birth and death queueing models
8.3.4 a shoe shine shop
8.3.5 a queueing system with bulk service
8.4 network of queues
8.4.1 open systems
8.4.2 closed systems
8.5 the system m/g/1
8.5.1 preliminaries: work and another cost identity
8.5.2 application of work to m/g/1
8.5.3 busy periods
8.6 variations on the m/g/1
8.6.1 the m/g/1 with random-sized batch arrivals
8.6.2 priority queues
8.6.3 an m/g/1 optimization example
8.6.4 the m/g/1 queue with server breakdown
8.7 the model g/m/1
8.7.1 the g/m/1 busy and idle periods
8.8 a finite source model
8.9 multiserver queues
8.9.1 erlang's loss system
8.9.2 the m/m/k queue
8.9.3 the g/m/k queue
8.9.4 the m/g/k queue
exercises
references
9 reliability theory
9.1 introduction
9.2 structure functions
9.2.1 minimal path and minimal cut sets
9.3 reliability of systems of independent components
9.4 bounds on the reliability function
9.4.1 method of inclusion and exclusion
9.4.2 second method for obtaining bounds on r(p)
9.5 system life as a function of component lives
9.6 expected system lifetime
9.6.1 an upper bound on the expected life of a parallel system
9.7 systems with repair
9.7.1 a series model with suspended animation
exercises
references
10 brownian motion and stationary processes
10.1 brownian motion
10.2 hitting times, maximum variable, and the gambler's ruin problem
10.3 variations on brownian motion
10.3.1 brownian motion with drift
10.3.2 geometric brownian motion
10.4 pricing stock options
10.4.1 an example in options pricing
10.4.2 the arbitrage theorem
10.4.3 the black-scholes option pricing formula
10.5 white noise
10.6 gaussian processes
10.7 stationary and weakly stationary processes
10.8 harmonic analysis of weakly stationary processes
exercises
references
11 simulation
11.1 introduction
11.2 general techniques for simulating continuous random variables
11.2.1 the inverse transformation method
11.2.2 the rejection method
11.2.3 the hazard rate method
11.3 special techniques for simulating continuous random variables
11.3.1 the normal distribution
11.3.2 the gamma distribution
11.3.3 the chi-squared distribution
11.3.4 the beta (n, m) distribution
11.3.5 the exponential distribution-the von neumann algorithm
11.4 simulating from discrete distributions
11.4.1 the alias method
11.5 stochastic processes
11.5.1 simulating a nonhomogeneous poisson process
11.5.2 simulating a two-dimensional poisson process
11.6 variance reduction techniques
11.6.1 use of antithetic variables
11.6.2 variance reduction by conditioning
11.6.3 control variates
11.6.4 importance sampling
11.7 determining the number of runs
11.8 generating from the stationary distribution of a markov chain
11.8.1 coupling from the past
11.8.2 another approach
exercises
references
appendix: solutions to starred exercises
index
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