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内容大纲
这是一部被广泛认可的关于贝叶斯方法的最领先的读本，因为其易于理解、分析数据和解决研究问题的实际操作性强而广受赞誉。贝叶斯数据分析，第三版应用最新的贝叶斯方法，继续采用实用的方法来分析数据。作者均是统计界的领导人物，在呈现更高等的方法之前，从数据分析的观点引进基本概念。整本书从始至终，从实际应用和研究中提取的大量的练习实例强调了贝叶斯推理在实践中的应用。 -
作者介绍
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目录
Preface
Part1:FundamentalsofBayesian1nference
1 Probabilityandinference
1.1 ThethreestepsofBayesiandataanalysis
1.2 Generalnotationforstatisticalinference
1.3 Bayesianinference
1.4 Discreteexamples:geneticsandspellchecking
1.5 Probabilityasameasureofuncertainty
1.6 Example:probabilitiesfromfootballpointspreads
1.7 Example:calibrationforrecordlinkage
1.8 Someusefulresultsfromprobabilitytheory
1.9 Computationandsoftware
1.10 Bayesianinferenceinappliedstatistics
1.11 Bibliographicnote
1.12 Exercises
2 Single-parametermodels
2.1 Estimatingaprobabilityfrombinomialdata
2.2 Posteriorascompromisebetweendataandpriorinformation
2.3 Summarizingposteriorinference
2.4 1nformativepriordistributions
2.5 Normaldistributionwithknownvariance
2.6 Otherstandardsingle-parametermodels
2.7 Example:informativepriordistributionforcancerrates
2.8 Noninformativepriordistributions
2.9 Weaklyinformativepriordistributions
2.10 Bibliographicnote
2.11 Exercises
3 1ntroductiontomultiparametermodels
3.1 Averagingover'nuisanceparameters'
3.2 Normaldatawithanoninformativepriordistribution
3.3 Normaldatawithaconjugatepriordistribution
3.4 Multinomialmodelforcategoricaldata
3.5 Multivariatenormalmodelwithknownvariance
3.6 Multivariatenormalwithunknownmeanandvariance
3.7 Example:analysisofabioassayexperiment
3.8 Summaryofelementarymodelingandcomputation
3.9 Bibliographicnote
3.10 Exercises
4 Asymptoticsandconnectionstonon-Bayesianapproaches
4.1 Normalapproximationstotheposteriordistribution
4.2 Large-sampletheory
4.3 Counterexamplestothetheorems
4.4 FrequencyevaluationsofBayesianinferences
4.5 Bayesianinterpretationsofotherstatisticalmethods
4.6 Bibliographicnote
4.7 Exercises
5 Hierarchicalmodels
5.1 Constructingaparameterizedpriordistribution
5.2 Exchangeabilityandhierarchicalmodels
5.3 Bayesiananalysisofconjugatehierarchicalmodels
5.4 Normalmodelwithexchangeableparameters
5.5 Example:parallelexperimentsineightschools
5.6 Hierarchicalmodelingappliedtoameta-analysis
5.7 Weaklyinformativepriorsforvarianceparameters
5.8 Bibliographicnote
5.9 Exercises
Part11:FundamentalsofBayesianDataAnalysis
6 Modelchecking
6.1 TheplaceofmodelcheckinginappliedBayesianstatistics
6.2 Dotheinferencesfromthemodelmakesense?
6.3 Posteriorpredictivechecking
6.4 Graphicalposteriorpredictivechecks
6.5 Modelcheckingfortheeducationaltestingexample
6.6 Bibliographicnote
6.7 Exercises
7 Evaluating，comparing，andexpandingmodels
7.1 Measuresofpredictiveaccuracy
7.2 1nformationcriteriaandcross-validation
7.3 Modelcomparisonbasedonpredictiveperformance
7.4 ModelcomparisonusingBayesfactors
7.5 Continuousmodelexpansion
7.6 1mplicitassumptionsandmodelexpansion:anexample
7.7 Bibliographicnote
7.8 Exercises
8 Modelingaccountingfordatacollection
8.1 Bayesianinferencerequiresamodelfordatacollection
8.2 Data-collectionmodelsandignorability
8.3 Samplesurveys
8.4 Designedexperiments
8.5 Sensitivityandtheroleofrandomization
8.6 Observationalstudies
8.7 Censoringandtruncation
8.8 Discussion
8.9 Bibliographicnote
8.10 Exercises
9 Decisionanalysis
9.1 Bayesiandecisiontheoryindifferentcontexts
9.2 Usingregressionpredictions:surveyincentives
9.3 Multistagedecisionmaking:medicalscreening
9.4 Hierarchicaldecisionanalysisforhomeradon
9.5 Personalvs.institutionaldecisionanalysis
9.6 Bibliographicnote
9.7 Exercises
Part111:AdvancedComputation
10 1ntroductiontoBayesiancomputation
10.1 Numericalintegration
10.2 Distributionalapproximations
10.3 Directsimulationandrejectionsampling
10.4 1mportancesampling
10.5 Howmanysimulationdrawsareneeded?
10.6 Computingenvironments
10.7 DebuggingBayesiancomputing
10.8 Bibliographicnote
10.9 Exercises
11 BasicsofMarkovchainsimulation
11.1 Gibbssampler
11.2 MetropolisandMetropolis-Hastingsalgorithms
11.3 UsingGibbsandMetropolisasbuildingblocks
11.4 1nferenceandassessingconvergence
11.5 Effectivenumberofsimulationdraws
11.6 Example:hierarchicalnormalmodel
11.7 Bibliographicnote
11.8 Exercises
12 ComputationallyefficientMarkovchainsimulation
12.1 EfficientGibbssamplers
12.2 EfficientMetropolisjumpingrules
12.3 FurtherextensionstoGibbsandMetropolis
12.4 HamiltonianMonteCarlo
12.5 HamiltonianMonteCarloforahierarchicalmodel
12.6 Stan:developingacomputingenvironment
12.7 Bibliographicnote
12.8 Exercises
13 Modalanddistributionalapproximations
13.1 Findingposteriormodes
13.2 Boundary-avoidingpriorsformodalsummaries
13.3 Normalandrelatedmixtureapproximations
13.4 FindingmarginalposteriormodesusingEM
13.5 Conditionalandmarginalposteriorapproximations
13.6 Example:hierarchicalnormalmodel（continued）
13.7 Variationalinference
13.8 Expectationpropagation
13.9 Otherapproximations
13.10 Unknownnormalizingfactors
13.11 Bibliographicnote
13.12 Exercises
Part1V:RegressionModels
14 1ntroductiontoregressionmodels
14.1 Conditionalmodeling
14.2 Bayesiananalysisofclassicalregression
14.3 Regressionforcausalinference:incumbencyandvoting
14.4 Goalsofregressionanalysis
14.5 Assemblingthematrixofexplanatoryvariables
14.6 Regularizationanddimensionreduction
14.7 Unequalvariancesandcorrelations
14.8 1ncludingnumericalpriorinformation
14.9 Bibliographicnote
14.10 Exercises
15 Hierarchicallinearmodels
15.1 Regressioncoefficientsexchangeableinbatches
15.2 Example:forecastingU.S.presidentialelections
15.3 1nterpretinganormalpriordistributionasextradata
15.4 Varyinginterceptsandslopes
15.5 Computation:batchingandtransformation
15.6 Analysisofvarianceandthebatchingofcoefficients
15.7 Hierarchicalmodelsforbatchesofvariancecomponents
15.8 Bibliographicnote
15.9 Exercises
16 Generalizedlinearmodels
16.1 Standardgeneralizedlinearmodellikelihoods
16.2 Workingwithgeneralizedlinearmodels
16.3 Weaklyinformativepriorsforlogisticregression
16.4 OverdispersedPoissonregressionforpolicestops
16.5 State-levelopinonsfromnationalpolls
16.6 Modelsformultivariateandmultinomialresponses
16.7 Loglinearmodelsformultivariatediscretedata
16.8 Bibliographicnote
16.9 Exercises
17 Modelsforrobustinference
17.1 Aspectsofrobustness
17.2 Overdispersedversionsofstandardmodels
17.3 Posteriorinferenceandcomputation
17.4 Robustinferencefortheeightschools
17.5 Robustregressionusingt-distributederrors
17.6 Bibliographicnote
17.7 Exercises
18 Modelsformissingdata
18.1 Notation
18.2 Multipleimputation
18.3 Missingdatainthemultivariatenormalandtmodels
18.4 Example:multipleimputationforaseriesofpolls
18.5 Missingvalueswithcounteddata
18.6 Example:anopinionpollinSlovenia
18.7 Bibliographicnote
18.8 Exercises
PartV:NonlinearandNonparametricModels
19 Parametricnonlinearmodels
19.1 Example:serialdilutionassay
19.2 Example:populationtoxicokinetics
19.3 Bibliographicnote
19.4 Exercises
20 Basisfunctionmodels
20.1 Splinesandweightedsumsofbasisfunctions
20.2 Basisselectionandshrinkageofcoefficients
20.3 Non-normalmodelsandregressionsurfaces
20.4 Bibliographicnote
20.5 Exercises
21 Gaussianprocessmodels
21.1 Gaussianprocessregression
21.2 Example:birthdaysandbirthdates
21.3 LatentGaussianprocessmodels
21.4 Functionaldataanalysis
21.5 Densityestimationandregression
21.6 Bibliographicnote
21.7 Exercises
22 Finitemixturemodels
22.1 Settingupandinterpretingmixturemodels
22.2 Example:reactiontimesandschizophrenia
22.3 Labelswitchingandposteriorcomputation
22.4 Unspecifiednumberofmixturecomponents
22.5 Mixturemodelsforclassificationandregression
22.6 Bibliographicnote
22.7 Exercises
23 Dirichletprocessmodels
23.1 Bayesianhistograms
23.2 Dirichletprocesspriordistributions
23.3 Dirichletprocessmixtures
23.4 Beyonddensityestimation
23.5 Hierarchicaldependence
23.6 Densityregression
23.7 Bibliographicnote
23.8 Exercises
Appendixes
A Standardprobabilitydistributions
A.1 Continuousdistributions
A.2 Discretedistributions
A.3 Bibliographicnote
B Outlineofproofsoflimittheorems
B.1 Bibliographicnote
C ComputationinRandStan
C.1 GettingstartedwithRandStan
C.2 FittingahierarchicalmodelinStan
C.3 Directsimulation，Gibbs，andMetropolisinR
C.4 ProgrammingHamiltonianMonteCarloinR
C.5 Furthercommentsoncomputation
C.6 Bibliographicnote
References
Author 1ndex
Subject 1ndex
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