應用隨機過(guò)程 概率模型導論（英文版 第11版）簡(jiǎn)介，目錄書(shū)摘

編輯推薦:《應用隨機過(guò)程 概率模型導論》是國際知名統計學(xué)家Sheldon M. Ross所著(zhù)的關(guān)于基礎概率理論和隨機過(guò)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。
與其他隨機過(guò)程教材相比，本書(shū)非常強調實(shí)踐性，內含極其豐富的例子和習題，涵蓋了眾多學(xué)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，培養對概率理論、隨機過(guò)程的直觀(guān)感覺(jué)。對那些需要將概率理論應用于精算學(xué)、計算機科學(xué)、管理學(xué)和社會(huì )科學(xué)的讀者而言，本書(shū)是一本極好的教材或參考書(shū)。
第11版新增大量例子和習題，還對連續時(shí)間的馬爾可夫鏈、漂移布朗運動(dòng)等內容做了修訂，更加注重強化讀者的概率直觀(guān)。

內容簡(jiǎn)介:

　　《應用隨機過(guò)程 概率模型導論（英文版 第11版）》是一部經(jīng)典的隨機過(guò)程著(zhù)作，敘述深入淺出、涉及面廣。主要內容有隨機變量、條件期望、馬爾可夫鏈、指數分布、泊松過(guò)程、平穩過(guò)程、更新理論及排隊論等，也包括了隨機過(guò)程在物理、生物、運籌、網(wǎng)絡(luò )、遺傳、經(jīng)濟、保險、金融及可靠性中的應用。特別是有關(guān)隨機模擬的內容，給隨機系統運行的模擬計算提供了有力的工具。最新版還增加了不帶左跳的隨機徘徊和生滅排隊模型等內容。本書(shū)約有700道習題，其中帶星號的習題還提供了解答。

　　《應用隨機過(guò)程 概率模型導論（英文版 第11版）》可作為概率論與數理統計、計算機科學(xué)、保險學(xué)、物理學(xué)、社會(huì )科學(xué)、生命科學(xué)、管理科學(xué)與工程學(xué)等專(zhuān)業(yè)隨機過(guò)程基礎課教材。

作者簡(jiǎn)介:

　　Sheldon M. Ross，國際知名概率與統計學(xué)家，南加州大學(xué)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(xué)統計系，曾在加州大學(xué)伯克利分校任教多年。研究領(lǐng)域包括：隨機模型、仿真模擬、統計分析、金融數學(xué)等。Ross教授著(zhù)述頗豐，他的多種暢銷(xiāo)數學(xué)和統計教材均產(chǎn)生了世界性的影響，如《概率論基礎教程（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