編輯推薦:《應(yīng)用隨機(jī)過程 概率模型導(dǎo)論》是國際知名統(tǒng)計(jì)學(xué)家Sheldon M. Ross所著的關(guān)于基礎(chǔ)概率理論和隨機(jī)過程的經(jīng)典教材，被加州大學(xué)伯克利分校、哥倫比亞大學(xué)、普度大學(xué)、密歇根大學(xué)、俄勒岡州立大學(xué)、華盛頓大學(xué)等眾多國外知名大學(xué)所采用。
與其他隨機(jī)過程教材相比，本書非常強(qiáng)調(diào)實(shí)踐性，內(nèi)含極其豐富的例子和習(xí)題，涵蓋了眾多學(xué)科的各種應(yīng)用。作者富于啟發(fā)而又不失嚴(yán)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(jī)過程的直觀感覺。對那些需要將概率理論應(yīng)用于精算學(xué)、計(jì)算機(jī)科學(xué)、管理學(xué)和社會科學(xué)的讀者而言，本書是一本極好的教材或參考書。
第11版新增大量例子和習(xí)題，還對連續(xù)時(shí)間的馬爾可夫鏈、漂移布朗運(yùn)動等內(nèi)容做了修訂，更加注重強(qiáng)化讀者的概率直觀。

內(nèi)容簡介:

　　《應(yīng)用隨機(jī)過程 概率模型導(dǎo)論（英文版 第11版）》是一部經(jīng)典的隨機(jī)過程著作，敘述深入淺出、涉及面廣。主要內(nèi)容有隨機(jī)變量、條件期望、馬爾可夫鏈、指數(shù)分布、泊松過程、平穩(wěn)過程、更新理論及排隊(duì)論等，也包括了隨機(jī)過程在物理、生物、運(yùn)籌、網(wǎng)絡(luò)、遺傳、經(jīng)濟(jì)、保險(xiǎn)、金融及可靠性中的應(yīng)用。特別是有關(guān)隨機(jī)模擬的內(nèi)容，給隨機(jī)系統(tǒng)運(yùn)行的模擬計(jì)算提供了有力的工具。最新版還增加了不帶左跳的隨機(jī)徘徊和生滅排隊(duì)模型等內(nèi)容。本書約有700道習(xí)題，其中帶星號的習(xí)題還提供了解答。

　　《應(yīng)用隨機(jī)過程 概率模型導(dǎo)論（英文版 第11版）》可作為概率論與數(shù)理統(tǒng)計(jì)、計(jì)算機(jī)科學(xué)、保險(xiǎn)學(xué)、物理學(xué)、社會科學(xué)、生命科學(xué)、管理科學(xué)與工程學(xué)等專業(yè)隨機(jī)過程基礎(chǔ)課教材。

作者簡介:

　　Sheldon M. Ross，國際知名概率與統(tǒng)計(jì)學(xué)家，南加州大學(xué)工業(yè)工程與運(yùn)籌系系主任。1968年博士畢業(yè)于斯坦福大學(xué)統(tǒng)計(jì)系，曾在加州大學(xué)伯克利分校任教多年。研究領(lǐng)域包括：隨機(jī)模型、仿真模擬、統(tǒng)計(jì)分析、金融數(shù)學(xué)等。Ross教授著述頗豐，他的多種暢銷數(shù)學(xué)和統(tǒng)計(jì)教材均產(chǎn)生了世界性的影響，如《概率論基礎(chǔ)教程（第8版）》等。

目錄:1IntroductiontoProbabilityTheory
1.1Introduction
1.2SampleSpaceandEvents
1.3ProbabilitiesDefinedonEvents
1.4ConditionalProbabilities
1.5IndependentEvents
1.6Bayes'Formula
Exercises
References
2RandomVariables
2.1RandomVariables
2.2DiscreteRandomVariables
2.2.1TheBernoulliRandomVariable
2.2.2TheBinomialRandomVariable
2.2.3TheGeometricRandomVariable
2.2.4ThePoissonRandomVariable
2.3ContinuousRandomVariables
2.3.1TheUniformRandomVariable
2.3.2ExponentialRandomVariables
2.3.3GammaRandomVariables
2.3.4NormalRandomVariables
2.4ExpectationofaRandomVariable
2.4.1TheDiscreteCase
2.4.2TheContinuousCase
2.4.3ExpectationofaFunctionofaRandomVariable
2.5JointlyDistributedRandomVariables
2.5.1JointDistributionFunctions
2.5.2IndependentRandomVariables
2.5.3CovarianceandVarianceofSumsofRandomVariables
2.5.4JointProbabilityDistributionofFunctionsofRandomVariables
2.6MomentGeneratingFunctions
2.6.1TheJointDistributionoftheSampleMeanandSampleVariancefromaNormalPopulation
2.7TheDistributionoftheNumberofEventsthatOccur
2.8LimitTheorems
2.9StochasticProcesses
Exercises
References
3ConditionalProbabilityandConditionalExpectation
3.1Introduction
3.2TheDiscreteCase
3.3TheContinuousCase
3.4ComputingExpectationsbyConditioning
3.4.1ComputingVariancesbyConditioning
3.5ComputingProbabilitiesbyConditioning
3.6SomeApplications
3.6.1AListModel
3.6.2ARandomGraph
3.6.3UniformPriors,Polya'sUrnModel,andBose-EinsteinStatistics
3.6.4MeanTimeforPatterns
3.6.5Thek-RecordValuesofDiscreteRandomVariables
3.6.6LeftSkipFreeRandomWalks
3.7AnIdentityforCompoundRandomVariables
3.7.1PoissonCompoundingDistribution
3.7.2BinomialCompoundingDistribution
3.7.3ACompoundingDistributionRelatedtotheNegativeBinomial
Exercises
4MarkovChains
4.1Introduction
4.2Chapman-KolmogorovEquations
4.3ClassificationofStates
4.4Long-RunProportionsandLimitingProbabilities
4.4.1LimitingProbabilities
4.5SomeApplications
4.5.1TheGambler'sRuinProblem
4.5.2AModelforAlgorithmicEfficiency
4.5.3UsingaRandomWalktoAnalyzeaProbabilisticAlgorithmfortheSatisfiabilityProblem
4.6MeanTimeSpentinTransientStates
4.7BranchingProcesses
4.8TimeReversibleMarkovChains
4.9MarkovChainMonteCarloMethods
4.10MarkovDecisionProcesses
4.11HiddenMarkovChains
4.11.1PredictingtheStates
Exercises
References
5TheExponentialDistributionandthePoissonProcess
5.1Introduction
5.2TheExponentialDistribution
5.2.1Definition
5.2.2PropertiesoftheExponentialDistribution
5.2.3FurtherPropertiesoftheExponentialDistribution
5.2.4ConvolutionsofExponentialRandomVariables
5.3ThePoissonProcess
5.3.1CountingProcesses
5.3.2DefinitionofthePoissonProcess
5.3.3InterarrivalandWaitingTimeDistributions
5.3.4FurtherPropertiesofPoissonProcesses
5.3.5ConditionalDistributionoftheArrivalTimes
5.3.6EstimatingSoftwareReliability
5.4GeneralizationsofthePoissonProcess
5.4.1NonhomogeneousPoissonProcess
5.4.2CompoundPoissonProcess
5.4.3ConditionalorMixedPoissonProcesses
5.5RandomIntensityFunctionsandHawkesProcesses
Exercises
References
6Continuous-TimeMarkovChains
6.1Introduction
6.2Continuous-TimeMarkovChains
6.3BirthandDeathProcesses
6.4TheTransitionProbabilityFunctionPij(t)
6.5LimitingProbabilities
6.6TimeReversibility
6.7TheReversedChain
6.8Uniformization
6.9ComputingtheTransitionProbabilities
Exercises
References
7RenewalTheoryandItsApplications
7.1Introduction
7.2DistributionofN(t)
7.3LimitTheoremsandTheirApplications
7.4RenewalRewardProcesses
7.5RegenerativeProcesses
7.5.1AlternatingRenewalProcesses
7.6Semi-MarkovProcesses
7.7TheInspectionParadox
7.8ComputingtheRenewalFunction
7.9ApplicationstoPatterns
7.9.1PatternsofDiscreteRandomVariables
7.9.2TheExpectedTimetoaMaximalRunofDistinctValues
7.9.3IncreasingRunsofContinuousRandomVariables
7.10TheInsuranceRuinProblem
Exercises
References
8QueueingTheory
8.1Introduction
8.2Preliminaries
8.2.1CostEquations
8.2.2Steady-StateProbabilities
8.3ExponentialModels
8.3.1ASingle-ServerExponentialQueueingSystem
8.3.2ASingle-ServerExponentialQueueingSystemHavingFiniteCapacity
8.3.3BirthandDeathQueueingModels
8.3.4AShoeShineShop
8.3.5AQueueingSystemwithBulkService
8.4NetworkofQueues
8.4.1OpenSystems
8.4.2ClosedSystems
8.5TheSystemM/G/
8.5.1Preliminaries:WorkandAnotherCostIdentity
8.5.2ApplicationofWorktoM/G/
8.5.3BusyPeriods
8.6VariationsontheM/G/
8.6.1TheM/G/1withRandom-SizedBatchArrivals
8.6.2PriorityQueues
8.6.3AnM/G/1OptimizationExample
8.6.4TheM/G/1QueuewithServerBreakdown
8.7TheModelG/M/
8.7.1TheG/M/1BusyandIdlePeriods
8.8AFiniteSourceModel
8.9MultiserverQueues
8.9.1Erlang'sLossSystem
8.9.2TheM/M/kQueue
8.9.3TheG/M/kQueue
8.9.4TheM/G/kQueue
Exercises
References
9ReliabilityTheory
9.1Introduction
9.2StructureFunctions
9.2.MinimalPathandMinimalCutSets
9.3ReliabilityofSystemsofIndependentComponents
9.4BoundsontheReliabilityFunction
9.4.1MethodofInclusionandExclusion
9.4.2SecondMethodforObtainingBoundsonr(p)
9.5SystemLifeasaFunctionofComponentLives
9.6ExpectedSystemLifetime
9.6.1AnUpperBoundontheExpectedLifeofaParallelSystem
9.7SystemswithRepair
9.7.1ASeriesModelwithSuspendedAnimation
Exercises
References
10BrownianMotionandStationaryProcesses
10.1BrownianMotion
10.2HittingTimes,MaximumVariable,andtheGambler'sRuinProblem
10.3VariationsonBrownianMotion
10.3.1BrownianMotionwithDrift
10.3.2GeometricBrownianMotion
10.4PricingStockOptions
10.4.1AnExampleinOptionsPricing
10.4.2TheArbitrageTheorem
10.4.3TheBlack-ScholesOptionPricingFormula
10.5TheMaximumofBrownianMotionwithDrift
10.6WhiteNoise
10.7GaussianProcesses
10.8StationaryandWeaklyStationaryProcesses
10.9HarmonicAnalysisofWeaklyStationaryProcesses
Exercises
References
11Simulation
11.1Introduction
11.2GeneralTechniquesforSimulatingContinuousRandomVariables
11.2.1TheInverseTransformationMethod
11.2.2TheRejectionMethod
11.2.TheHazardRateMethod
11.3SpecialTechniquesforSimulatingContinuousRandomVariables
11.3.1TheNormalDistribution
11.3.2TheGammaDistribution
11.3.3TheChi-SquaredDistribution
11.3.4TheBeta(n,m)Distribution
11.3.5TheExponentialDistribution-TheVonNeumannAlgorithm
11.4SimulatingfromDiscreteDistributions
11.4.1TheAliasMethod
11.5StochasticProcesses
11.5.1SimulatingaNonhomogeneousPoissonProcess
11.5.2SimulatingaTwo-DimensionalPoissonProcess
11.6VarianceReductionTechniques
11.6.1UseofAntitheticVariables
11.6.2VarianceReductionbyConditioning
11.6.3ControlVariates
11.6.4ImportanceSampling
11.7DeterminingtheNumberofRuns
11.8GeneratingfromtheStationaryDistributionofaMarkovChain
11.8.1CouplingfromthePast
11.8.2AnotherApproach
Exercises
References
Appendix:SolutionstoStarredExercises
Index