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

建不了的模。

Chapter 52 TheMCMCProcedure Contents 9 R0 a( Q1 z0 @# ]' j  gOverview:MCMCProcedure ............................ 3478 % J- J& d: C. A3 s5 f5 WPROCMCMCComparedwithOtherSASProcedures ............3479 ( S/ g- I, g' T( @" M, NGettingStarted:MCMCProcedure .......................... 3479 8 B5 W- s* A" n+ L9 D" HSimpleLinearRegression ...........................3480 TheBehrens-FisherProblem ..........................3488 6 E" S/ w2 E# @: _) {Mixed-EffectsModel .............................3492 ; X5 u4 K% O% c* h, [# w# hSyntax:MCMCProcedure .............................. 3495   J0 k, G$ E! `- \8 \3 @- UPROCMCMCStatement ...........................3496 ARRAYStatement ...............................3508 BEGINCNST/ENDCNSTStatement .....................3509 BEGINNODATA/ENDNODATAStatements .................3511 BYStatement .................................3511 + H" ~* i% p* J5 M, T4 k. H3 |MODELStatement ...............................3512 7 I& d( W5 \9 l0 IPARMSStatement ...............................3515 3 G8 K8 A' f! x5 ^PRIOR/HYPERPRIORStatement .......................3516 ProgrammingStatements ...........................3516 4 r, w  p( R; E+ d1 IUDSStatement .................................3518 Details:MCMCProcedure .............................. 3522 ' a1 a6 u- H; e# {: ]% OHowPROCMCMCWorks ..........................3522 BlockingofParameters ............................3523 Samplers ....................................3524 7 ]9 {* ]2 I6 W0 cTuningtheProposalDistribution .......................3525 InitialValuesoftheMarkovChains ......................3528 % {3 X8 y% Y% g, \( N4 k% ]AssignmentsofParameters ..........................3528 . s; q6 C3 e4 K) I# o2 J" z; }) NStandardDistributions .............................3530 SpecifyingaNewDistribution .........................3541 UsingDensityFunctionsintheProgrammingStatements ...........3542 0 F+ D8 t+ ~6 I7 X& S( l; sTruncationandCensoring ...........................3544 , ~  B; }) ^) O8 c* Y* TMultivariateDensityFunctions ........................3546 - ]+ x) p, E  _/ ?  C& [# Q' {$ q9 oSomeUsefulSASFunctions ..........................3549 : V2 f& ~! q7 Y2 I% @MatrixFunctionsinPROCMCMC ......................3551 ModelingJointLikelihood ...........................3556 RegeneratingDiagnosticsPlots ........................3557 PosteriorPredictiveDistribution ........................3560 HandlingofMissingData ...........................3565 FloatingPointErrorsandOverflows ......................3565 # U- i9 z) s* [) |# PHandlingErrorMessages ...........................3568 " H+ W% r0 V9 ~) E9 o) zComputationalResources ...........................3570 DisplayedOutput ................................3571 ODSTableNames ...............................3575 ODSGraphics .................................3577 8 `$ L- L, S! \/ [. W/ mExamples:MCMCProcedure ............................ 3578 ' E- I9 o) z2 F% [& NExample52.1:SimulatingSamplesFromaKnownDensity .........3578 Example52.2:Box-CoxTransformation ...................3583 Example52.3:GeneralizedLinearModels ..................3592 / x5 ^% n' |: ~4 p: h' YExample52.4:NonlinearPoissonRegressionModels ............3605 Example52.5:Random-EffectsModels ...................3614 ' S4 s0 X" k( C7 b6 p) Y% C3 aExample52.6:ChangePointModels .....................3630 2 Y, Q) v6 T. x0 s* dExample52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 9 S0 _% {+ h6 }, W' kExample52.8:CoxModels ..........................3647 Example52.9:NormalRegressionwithIntervalCensoring .........3664 Example52.10:ConstrainedAnalysis ....................3666 3 v1 t  J3 b' H* ?5 jExample52.11:ImplementaNewSamplingAlgorithm ...........3672 * `7 A! Z2 _/ v0 O7 j% w' dExample52.12:UsingaTransformationtoImproveMixing .........3683 4 B6 S" C) V9 W, nExample52.13:Gelman-RubinDiagnostics .................3693 References ...................................... 3700 ( V/ f( l) e5 z& z# t% {8 _ 游客，如果您要查看本帖隐藏内容请回复

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