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

建不了的模。

Chapter 52 TheMCMCProcedure   Y- d+ Z7 G9 Y# ]' [Contents Overview:MCMCProcedure ............................ 3478 6 z3 L& }' ^% ZPROCMCMCComparedwithOtherSASProcedures ............3479 : f. O5 X" V- i5 s3 d+ I7 M& ^+ y! eGettingStarted:MCMCProcedure .......................... 3479 3 L, c) n2 D3 E4 m# GSimpleLinearRegression ...........................3480 TheBehrens-FisherProblem ..........................3488 7 X/ f2 s  K7 w3 C# Y0 v) J. rMixed-EffectsModel .............................3492 ( h3 P* O7 t7 B, Q1 b4 @( ASyntax:MCMCProcedure .............................. 3495 * n) |6 R9 t( W8 M: E' vPROCMCMCStatement ...........................3496 # t8 F! Z0 s: U7 m6 y( }ARRAYStatement ...............................3508 BEGINCNST/ENDCNSTStatement .....................3509 ( B% f* [  o) v- wBEGINNODATA/ENDNODATAStatements .................3511 BYStatement .................................3511 ' H; M, L* i6 \$ GMODELStatement ...............................3512 4 m; h% S, L) y/ g4 z) x( u  J4 }% |PARMSStatement ...............................3515 6 Q: ?) ~  ?) u+ e8 aPRIOR/HYPERPRIORStatement .......................3516 ProgrammingStatements ...........................3516 UDSStatement .................................3518 $ |. M5 O$ Y$ W+ jDetails:MCMCProcedure .............................. 3522 HowPROCMCMCWorks ..........................3522 BlockingofParameters ............................3523 . W# K0 j8 m; E8 ]# G/ m3 z  jSamplers ....................................3524 - A9 w9 c: V0 J( r! R/ f7 H8 nTuningtheProposalDistribution .......................3525 InitialValuesoftheMarkovChains ......................3528 AssignmentsofParameters ..........................3528 ! H5 u: v. P& N5 T+ A: B$ ?* qStandardDistributions .............................3530 - P: O& W" w2 I( q# f) D9 eSpecifyingaNewDistribution .........................3541 ! ]8 ?( v( j; o( RUsingDensityFunctionsintheProgrammingStatements ...........3542 TruncationandCensoring ...........................3544 8 n& ~; t7 x2 @MultivariateDensityFunctions ........................3546 SomeUsefulSASFunctions ..........................3549 MatrixFunctionsinPROCMCMC ......................3551 ModelingJointLikelihood ...........................3556 RegeneratingDiagnosticsPlots ........................3557 PosteriorPredictiveDistribution ........................3560 5 @' L7 ^( Q% `1 \8 g6 vHandlingofMissingData ...........................3565 FloatingPointErrorsandOverflows ......................3565 6 T+ O1 m6 u  y6 l# |0 \  wHandlingErrorMessages ...........................3568 ComputationalResources ...........................3570 DisplayedOutput ................................3571 # K8 Q' i/ E; dODSTableNames ...............................3575 ODSGraphics .................................3577 ; `* S; P# ?, E9 Y4 E: CExamples:MCMCProcedure ............................ 3578 Example52.1:SimulatingSamplesFromaKnownDensity .........3578 + T# E5 p6 {" A" E* _9 xExample52.2:Box-CoxTransformation ...................3583 Example52.3:GeneralizedLinearModels ..................3592 ; D' q1 \3 K9 W/ G6 s$ i: yExample52.4:NonlinearPoissonRegressionModels ............3605 Example52.5:Random-EffectsModels ...................3614 . S. [2 ?/ f( b' |3 uExample52.6:ChangePointModels .....................3630 Example52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 Example52.8:CoxModels ..........................3647 Example52.9:NormalRegressionwithIntervalCensoring .........3664 1 ]" G* P( H4 k( G& kExample52.10:ConstrainedAnalysis ....................3666 % K; s: D( R% Q3 y0 x7 B# n' Y! T: hExample52.11:ImplementaNewSamplingAlgorithm ...........3672 2 Z1 ]2 E7 ?+ P% j3 Q$ `Example52.12:UsingaTransformationtoImproveMixing .........3683 " v" a5 p5 j, U. JExample52.13:Gelman-RubinDiagnostics .................3693 References ...................................... 3700 游客，如果您要查看本帖隐藏内容请回复

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