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

建不了的模。

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Chapter 52 ) l+ [3 ]( `+ T$ `TheMCMCProcedure ) u( f, X. A5 k9 i# i  ?! ~Contents / T% D6 y7 X& d$ qOverview:MCMCProcedure ............................ 3478 PROCMCMCComparedwithOtherSASProcedures ............3479 & G( W" U: ^5 t  f8 }GettingStarted:MCMCProcedure .......................... 3479 4 ?# P- ~% X: j6 y+ l! ^# QSimpleLinearRegression ...........................3480 TheBehrens-FisherProblem ..........................3488 Mixed-EffectsModel .............................3492 9 j) o0 r6 n" g  YSyntax:MCMCProcedure .............................. 3495 PROCMCMCStatement ...........................3496 5 s8 R5 W/ f4 {: f+ lARRAYStatement ...............................3508 6 L! T' I3 w: d. V. V+ F! }BEGINCNST/ENDCNSTStatement .....................3509 / J5 q  s5 x9 [$ f2 |( k. hBEGINNODATA/ENDNODATAStatements .................3511 , t- `! \. F0 A$ S# h! @& ]BYStatement .................................3511 2 Q4 F& o' _" dMODELStatement ...............................3512 PARMSStatement ...............................3515 PRIOR/HYPERPRIORStatement .......................3516 ProgrammingStatements ...........................3516 & |  m1 g+ W+ ]& n4 qUDSStatement .................................3518 " e* m6 w, X$ sDetails:MCMCProcedure .............................. 3522 HowPROCMCMCWorks ..........................3522 1 m6 A3 G8 s5 G" E/ n/ w5 ^# D. ?BlockingofParameters ............................3523 Samplers ....................................3524 TuningtheProposalDistribution .......................3525 InitialValuesoftheMarkovChains ......................3528 AssignmentsofParameters ..........................3528 StandardDistributions .............................3530 6 V3 r4 f  j5 H( {SpecifyingaNewDistribution .........................3541 / Q0 b1 u3 q3 b+ ]+ U& g8 z1 kUsingDensityFunctionsintheProgrammingStatements ...........3542 TruncationandCensoring ...........................3544 MultivariateDensityFunctions ........................3546 : {' W) a, n# {5 L# tSomeUsefulSASFunctions ..........................3549 7 A, e. ~! R# q& \/ h- rMatrixFunctionsinPROCMCMC ......................3551 7 H) J: }. [( SModelingJointLikelihood ...........................3556 : ^5 s9 [% C! ~4 x) CRegeneratingDiagnosticsPlots ........................3557 PosteriorPredictiveDistribution ........................3560 HandlingofMissingData ...........................3565 FloatingPointErrorsandOverflows ......................3565 ! l/ e- Y7 h* u* P0 jHandlingErrorMessages ...........................3568 ComputationalResources ...........................3570 ; |9 l# e  A! U* G  M1 Z0 ]DisplayedOutput ................................3571 ODSTableNames ...............................3575   {1 |: M/ H) h' W0 ?( F7 i$ m8 sODSGraphics .................................3577 Examples:MCMCProcedure ............................ 3578 2 h9 U* y' `( R4 P) MExample52.1:SimulatingSamplesFromaKnownDensity .........3578 Example52.2:Box-CoxTransformation ...................3583 0 a. V1 g- f0 |2 |5 S9 ^4 @Example52.3:GeneralizedLinearModels ..................3592 $ j: M3 {& s, \; G0 fExample52.4:NonlinearPoissonRegressionModels ............3605 Example52.5:Random-EffectsModels ...................3614 8 I+ x# Z  T8 mExample52.6:ChangePointModels .....................3630 Example52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 / E$ o* T4 X/ iExample52.8:CoxModels ..........................3647 Example52.9:NormalRegressionwithIntervalCensoring .........3664 Example52.10:ConstrainedAnalysis ....................3666 # U( T+ t( J3 ~1 ~2 _Example52.11:ImplementaNewSamplingAlgorithm ...........3672 Example52.12:UsingaTransformationtoImproveMixing .........3683 Example52.13: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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