[教程] SAS_STAT9.2 User’s Guide, Second Edition The MCMC Procedure

建不了的模。

Chapter 52 TheMCMCProcedure 5 I7 s, Z9 d3 ?! C5 P6 u* JContents Overview:MCMCProcedure ............................ 3478 7 C, }1 P% [/ K/ e/ vPROCMCMCComparedwithOtherSASProcedures ............3479 GettingStarted:MCMCProcedure .......................... 3479 SimpleLinearRegression ...........................3480 TheBehrens-FisherProblem ..........................3488 Mixed-EffectsModel .............................3492 . M* C# \. F4 o7 t1 I  tSyntax:MCMCProcedure .............................. 3495 1 G, o8 M) J5 _! F  A! ]0 APROCMCMCStatement ...........................3496 ARRAYStatement ...............................3508 5 _: ?& S% _3 {. v- dBEGINCNST/ENDCNSTStatement .....................3509 BEGINNODATA/ENDNODATAStatements .................3511 BYStatement .................................3511 # n) g0 c& T; M4 e9 ~MODELStatement ...............................3512 PARMSStatement ...............................3515 PRIOR/HYPERPRIORStatement .......................3516 ProgrammingStatements ...........................3516 * P3 n7 m4 t. oUDSStatement .................................3518 2 k6 {* \0 u9 y8 U5 FDetails:MCMCProcedure .............................. 3522 HowPROCMCMCWorks ..........................3522 ( w. O& V" D$ V; F' I, a  SBlockingofParameters ............................3523 Samplers ....................................3524 TuningtheProposalDistribution .......................3525 InitialValuesoftheMarkovChains ......................3528 AssignmentsofParameters ..........................3528 StandardDistributions .............................3530 SpecifyingaNewDistribution .........................3541 : ~2 E% m8 o, R" ~UsingDensityFunctionsintheProgrammingStatements ...........3542 1 Q! T% {' ^' ^, V( t* g% q* B/ KTruncationandCensoring ...........................3544 MultivariateDensityFunctions ........................3546 5 j1 B; ?3 a" H5 b# \: P$ E! Y+ sSomeUsefulSASFunctions ..........................3549 MatrixFunctionsinPROCMCMC ......................3551 ModelingJointLikelihood ...........................3556 RegeneratingDiagnosticsPlots ........................3557 ! ?* ~$ ?" w  H2 [4 Z/ d1 _PosteriorPredictiveDistribution ........................3560 5 Q. Q- {9 U; O6 |. }HandlingofMissingData ...........................3565 FloatingPointErrorsandOverflows ......................3565 , c! @; Z( M5 nHandlingErrorMessages ...........................3568 ComputationalResources ...........................3570 5 Q0 w% w5 R( W% q  r) E( H# l5 ?- R! DDisplayedOutput ................................3571 ODSTableNames ...............................3575 1 A, a4 E' [& I9 X% v& YODSGraphics .................................3577 Examples:MCMCProcedure ............................ 3578 Example52.1:SimulatingSamplesFromaKnownDensity .........3578 " G- Y+ @3 v* NExample52.2:Box-CoxTransformation ...................3583 Example52.3:GeneralizedLinearModels ..................3592 : t4 G+ s0 x; BExample52.4:NonlinearPoissonRegressionModels ............3605 * g- R' U$ D" H: \Example52.5:Random-EffectsModels ...................3614 2 `8 m  G9 P/ z* Z: r  q6 fExample52.6:ChangePointModels .....................3630 Example52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 Example52.8:CoxModels ..........................3647 Example52.9:NormalRegressionwithIntervalCensoring .........3664 Example52.10:ConstrainedAnalysis ....................3666 Example52.11:ImplementaNewSamplingAlgorithm ...........3672 + l7 ]/ l% t6 f! n$ E, l( TExample52.12:UsingaTransformationtoImproveMixing .........3683   C. o0 |+ t9 m( B2 GExample52.13:Gelman-RubinDiagnostics .................3693 References ...................................... 3700 2 i7 d/ G, {" ~ 游客，如果您要查看本帖隐藏内容请回复

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