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

建不了的模。

Chapter 52 ) M# ?6 N' W6 e$ g. {/ w. D/ S# MTheMCMCProcedure Contents Overview:MCMCProcedure ............................ 3478 PROCMCMCComparedwithOtherSASProcedures ............3479 GettingStarted:MCMCProcedure .......................... 3479 SimpleLinearRegression ...........................3480 ) j" v& C( z9 i4 @: \4 U) ~9 aTheBehrens-FisherProblem ..........................3488 Mixed-EffectsModel .............................3492 & {% H" Y/ i+ B6 rSyntax:MCMCProcedure .............................. 3495 ; b9 B! t3 E  H9 Z! _PROCMCMCStatement ...........................3496 ARRAYStatement ...............................3508 0 f# b+ ?$ b1 e4 ]$ t2 ?* VBEGINCNST/ENDCNSTStatement .....................3509 BEGINNODATA/ENDNODATAStatements .................3511 " K7 O2 |, i" d. t4 C# x) M+ L8 d6 HBYStatement .................................3511 MODELStatement ...............................3512 PARMSStatement ...............................3515 1 |" D2 c! V' A) @PRIOR/HYPERPRIORStatement .......................3516 # h8 u' T5 m( m8 sProgrammingStatements ...........................3516 * D# I. `- x+ {4 lUDSStatement .................................3518 / G  G; Y4 t: |( L$ |Details:MCMCProcedure .............................. 3522 : L! b9 t" {9 O8 QHowPROCMCMCWorks ..........................3522 " U& m6 G& h# n0 y+ p9 V# y8 kBlockingofParameters ............................3523 Samplers ....................................3524 2 K% ~4 d( \" ^6 q$ `) w+ Q! oTuningtheProposalDistribution .......................3525 InitialValuesoftheMarkovChains ......................3528 AssignmentsofParameters ..........................3528 ! c$ M& u% B0 F/ |  [  ZStandardDistributions .............................3530 SpecifyingaNewDistribution .........................3541 UsingDensityFunctionsintheProgrammingStatements ...........3542 ( ?$ p* |- J! I# C4 oTruncationandCensoring ...........................3544 MultivariateDensityFunctions ........................3546 " w) H( D" |* a( h0 \! p5 L' H: S+ }SomeUsefulSASFunctions ..........................3549 MatrixFunctionsinPROCMCMC ......................3551 ModelingJointLikelihood ...........................3556 ' O% J$ V0 p3 b3 J/ a; _% FRegeneratingDiagnosticsPlots ........................3557 PosteriorPredictiveDistribution ........................3560 6 A; e4 g- \( W: e. u  c- ~+ mHandlingofMissingData ...........................3565 FloatingPointErrorsandOverflows ......................3565 HandlingErrorMessages ...........................3568 ComputationalResources ...........................3570 ! |, \9 H( h4 G) I  L) d8 o: D4 ]DisplayedOutput ................................3571 $ o4 V: ]2 [2 j" p4 U6 vODSTableNames ...............................3575 ODSGraphics .................................3577 ; R# y$ U. a; n! n3 DExamples:MCMCProcedure ............................ 3578 9 J' ], V7 j, O3 B" WExample52.1:SimulatingSamplesFromaKnownDensity .........3578 5 c0 h! z5 {' R5 C' oExample52.2:Box-CoxTransformation ...................3583 5 s4 }, ~- f$ O( k  [Example52.3:GeneralizedLinearModels ..................3592 Example52.4:NonlinearPoissonRegressionModels ............3605 / ^+ x' j% R, U; ?1 yExample52.5:Random-EffectsModels ...................3614 " o+ R, r/ _. u" V) VExample52.6:ChangePointModels .....................3630 5 a4 A: \6 \1 c3 ZExample52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 Example52.8:CoxModels ..........................3647 ( E; [2 m6 X6 V* T8 a8 u; C. XExample52.9:NormalRegressionwithIntervalCensoring .........3664 1 k: {$ E- Q- s% t, DExample52.10:ConstrainedAnalysis ....................3666 8 W3 L% Y5 P6 O& e+ _5 V7 F2 SExample52.11:ImplementaNewSamplingAlgorithm ...........3672 8 T7 ?1 `$ l) |1 h$ e* G& w/ B  NExample52.12:UsingaTransformationtoImproveMixing .........3683 Example52.13:Gelman-RubinDiagnostics .................3693 # ^! u' ]! [2 IReferences ...................................... 3700 , A( a$ P  Y1 Z/ V1 v- T 游客，如果您要查看本帖隐藏内容请回复

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