蒙特卡罗统计方法
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库存2件
作者[法]罗伯特 著
出版社世界图书出版公司
出版时间2009-10
版次2
装帧平装
货号141
上书时间2024-08-11
商品详情
- 品相描述:九品
图书标准信息
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作者
[法]罗伯特 著
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出版社
世界图书出版公司
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出版时间
2009-10
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版次
2
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ISBN
9787510005114
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定价
79.00元
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装帧
平装
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开本
16开
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纸张
胶版纸
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页数
645页
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正文语种
英语
- 【内容简介】
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Itisatributetoourprofessionthatatextbookthatwascurrentin1999isstartingtofeelold.TheworkforthefirsteditionofMonteCarloStatisticalMethods(MCSM1)wasfinishedinlate1998,andtheadvancesmadesincethen,aswellasourlevelofunderstandingofMonteCarlomethods,havegrownagreatdeal.Moreover,twootherthingshavehappened.TopicsthatjustmadeitintoMCSM1withthebriefesttreatment(forexample,perfectsampling)havenowattainedalevelofimportancethatnecessitatesamuchmorethoroughtreatment.Secondly,someothermethodshavenotwithstoodthetestoftimeor,perhaps,havenotyetbeenfullydeveloped,andnowreceiveamoreappropriatetreatment.
WhenweworkedonMCSM1inthemid-to-late90s,MCMCalgorithmswerealreadyheavilyused,andtheflowofpublicationsonthistopicwasatsuchahighlevelthatthepicturewasnotonlyrapidlychanging,butalsonecessarilyincomplete.Thus,theprocessthatwefollowedinMCSM1wasthatofsomeonewhowasthrownintotheoceanandwastryingtograbontothebiggestandmostseeminglyusefulobjectswhiletryingtoseparatetheflotsamfromthejetsam.Nonetheless,wealsofeltthatthefundamentalsofmanyofthesealgorithmswereclearenoughtobecoveredatthetextbookalevel,sowe"swamon.
- 【作者简介】
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作者:(法国)罗伯特(ChristianP.Robert)(法国)GeorgeCasella
- 【目录】
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PrefacetotheSecondEdition
PrefacetotheFirstEdition
1Introduction
1.1StatisticalModels
1.2LikelihoodMethods
1.3BayesianMethods
1.4DeterministicNumericalMethods
1.4.1Optimization
1.4.2Integration
1.4.3Comparison
1.5Problems
1.6Notes
1.6.1PriorDistributions
1.6.2BootstrapMethods
2RandomVariableGeneration
2.1Introduction
2.1.1UniformSimulation
2.1.2TheInverseTransform
2.1.3Alternatives
2.1.4OptimalAlgorithms
2.2GeneralTransformationMethods
2.3Accept-RejectMethods
2.3.1TheFundamentalTheoremofSimulation
2.3.2TheAccept-RejectAlgorithm
2.4EnvelopeAccept-RejectMethods
2.4.1TheSqueezePrinciple
2.4.2Log-ConcaveDensities
2.5Problems
2.6Notes
2.6.1TheKissGenerator
2.6.2Quasi-MonteCarloMethods
2.6.3MixtureRepresentatiOnS
3MonteCarloIntegration
3.1IntroduCtion
3.2ClassicalMonteCarloIntegration
3.3ImportanceSampling
3.3.1Principles
3.3.2FiniteVarianceEstimators
3.3.3ComparingImportanceSamplingwithAccept-Reject
3.4LaplaceApproximations
3.5Problems
3.6Notes
3.6.1LargeDeviationsTechniques
3.6.2TheSaddlepointApproximation
4ControlingMonteCarloVariance
4.1MonitoringVariationwiththeCLT
4.1.1UnivariateMonitoring
4.1.2MultivariateMonitoring
4.2Rao-Blackwellization
4.3RiemannApproximations
4.4AccelerationMethods
4.4.1AntitheticVariables
4.4.2Contr01Variates
4.5Problems
4.6Notes
4.6.1MonitoringImportanceSamplingConvergence
4.6.2Accept-RejectwithLooseBounds
4.6.3Partitioning
5MonteCarloOptimization
5.1Introduction
5.2StochasticExploration
5.2.1ABasicSolution
5.2.2GradientMethods
5.2.3SimulatedAnnealing
5.2.4PriorFeedback
5.3StochasticApproximation
5.3.1MissingDataModelsandDemarginalization
5.3.2ThcEMAlgorithm
5.3.3MonteCarloEM
5.3.4EMStandardErrors
5.4Problems
5.5Notes
5.5.1VariationsonEM
5.5.2NeuralNetworks
5.5.3TheRobbins-Monroprocedure
5.5.4MonteCarloApproximation
6MarkovChains
6.1EssentialsforMCMC
6.2BasicNotions
6.3Irreducibility,Atoms,andSmallSets
6.3.1Irreducibility
6.3.2AtomsandSmallSets
6.3.3CyclesandAperiodicity
6.4TransienceandRecurrence
6.4.1ClassificationofIrreducibleChains
6.4.2CriteriaforRecurrence
6.4.3HarrisRecurrence
6.5InvariantMeasures
6.5.1StationaryChains
6.5.2Kac’sTheorem
6.5.3ReversibilityandtheDetailedBalanceCondition
6.6ErgodicityandConvergence
6.611Ergodicity
6.6.2GeometricConvergence
6.6.3UniformErgodicity
6.7LimitTheorems
6.7.1ErgodicTheorems
6.7.2CentralLimitTheorems
6.8Problems
6.9Notes
6.9.1Dri允Conditions
6.9.2Eaton’SAdmissibilityCondition
6.9.3AlternativeConvergenceConditions
6.9.4MixingConditionsandCentralLimitTheorems
6.9.5CovarianceinMarkovChains
7TheMetropolis-HastingsAlgorithm
7.1TheMCMCPrinciple
7.2MonteCarloMethodsBasedonMarkovChains
7.3TheMetropolis-Hastingsalgorithm
7.3.1Definition
7.3.2ConvergenceProperties
7.4TheIndependentMetropolis-HastingsAlgorithm
7.4.1FixedProposals
7.4.2AMetropolis-HastingsVersionofARS
7.5Randomwalks
7.6OptimizationandContr01
7.6.1OptimizingtheAcceptanceRate
7.6.2ConditioningandAccelerations
7.6.3AdaptiveSchemes
7.7Problems
7.8Nores
7.8.1BackgroundoftheMetropolisAlgorithm
7.8.2GeometricConvergenceofMetropolis-HastingsAlgorithms
7.8.3AReinterpretationofSimulatedAnnealing
7.8.4RCferenceAcceptanceRates
7.8.5LangevinAlgorithms
8TheSliceSampler
8.1AnotherLookattheFundamentalTheorem
8.2TheGeneralSliceSampler
8.3ConvergencePropertiesoftheSliceSampler
8.4Problems
8.5Notes
8.5.1DealingwithDi伍cultSlices
9TheTwo-StageGibbsSampler
9.1AGeneralClassofTwo-StageAlgorithms
9.1.1FromSliceSamplingtoGibbsSampling
9.1.2Definition
9.1.3BacktotheSliceSampler
9.1.4TheHammersley-CliffordTheorem
9.2FundamentalProperties
9.2.1ProbabilisticStructures
9.2.2ReversibleandInterleavingChains
9.2.3TheDualityPrinciple
9.3MonotoneCovarianceandRao-Btackwellization
9.4TheEM-GibbsConnection
9.5Transition
9.6Problems
9.7Notes
9.7.1InferenceforMixtures
9.7.2ARCHModels
10TheMulti-StageGibbsSampler
10.1BasicDerivations
10.1.1Definition
10.1.2Completion
……
11VariableDimensionModelsandReversibleJumpAlgorithms
12DiagnosingConvergence
13PerfectSampling
14IteratedandSequentialImportanceSampling
AProbabilityDistributions
BNotation
References
IndexofNames
IndexofSubjects
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