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

建不了的模。

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Chapter 52 - P/ h; }( D0 aTheMCMCProcedure Contents Overview:MCMCProcedure ............................ 3478 $ q" d0 L4 f% h7 o- F  r9 Y$ J6 nPROCMCMCComparedwithOtherSASProcedures ............3479 . [5 p( m; m2 [* u9 F8 P* ~GettingStarted:MCMCProcedure .......................... 3479 SimpleLinearRegression ...........................3480 TheBehrens-FisherProblem ..........................3488 5 G, [: S) C2 O* r5 \Mixed-EffectsModel .............................3492 8 h5 I! |9 _% J5 XSyntax:MCMCProcedure .............................. 3495 PROCMCMCStatement ...........................3496 ) g/ p4 A7 ?/ A  j, z% fARRAYStatement ...............................3508 BEGINCNST/ENDCNSTStatement .....................3509 2 u8 b6 b! z  Y9 y3 @2 vBEGINNODATA/ENDNODATAStatements .................3511 7 i3 [, u& n0 e. i0 p8 U; V  NBYStatement .................................3511 MODELStatement ...............................3512 0 b! S  g, {( S- ~) L1 M& R2 MPARMSStatement ...............................3515 PRIOR/HYPERPRIORStatement .......................3516 ProgrammingStatements ...........................3516 UDSStatement .................................3518   D% B6 [5 ]8 iDetails:MCMCProcedure .............................. 3522 HowPROCMCMCWorks ..........................3522 BlockingofParameters ............................3523 Samplers ....................................3524 1 o: Y2 H2 T( m, o# E0 u) [TuningtheProposalDistribution .......................3525 InitialValuesoftheMarkovChains ......................3528 ! W+ w9 n( _  D% w7 N! Q5 gAssignmentsofParameters ..........................3528 / m0 L5 P6 z3 b5 h4 |, RStandardDistributions .............................3530 + z# y: f- R8 S0 {SpecifyingaNewDistribution .........................3541 4 L9 y: ?0 j( U' S4 o- F: IUsingDensityFunctionsintheProgrammingStatements ...........3542 2 H$ g/ j0 M5 x5 ?& X0 j  B8 l( J; tTruncationandCensoring ...........................3544 MultivariateDensityFunctions ........................3546 8 o& _; P' r1 n) KSomeUsefulSASFunctions ..........................3549 MatrixFunctionsinPROCMCMC ......................3551 ModelingJointLikelihood ...........................3556 : d# M0 |6 Q, ?9 R) W( Z& {( s* p, ORegeneratingDiagnosticsPlots ........................3557 3 _% \; w9 H3 S4 j0 lPosteriorPredictiveDistribution ........................3560 2 ~9 E& u0 c2 l* r. z' MHandlingofMissingData ...........................3565 ) w5 T! n+ E7 s$ p. D3 |FloatingPointErrorsandOverflows ......................3565 9 D+ G$ v6 e0 S8 wHandlingErrorMessages ...........................3568 ComputationalResources ...........................3570 DisplayedOutput ................................3571 - v0 m/ b0 l8 d" E- n0 [ODSTableNames ...............................3575 ODSGraphics .................................3577 $ |5 j& ^+ k- X/ J3 @+ PExamples:MCMCProcedure ............................ 3578 $ Y; Z) r9 p" |: R" F( rExample52.1:SimulatingSamplesFromaKnownDensity .........3578 5 i  y/ Y% V# S! j! n: K% hExample52.2:Box-CoxTransformation ...................3583 Example52.3:GeneralizedLinearModels ..................3592 3 O1 b0 T* O4 p5 B, t3 BExample52.4:NonlinearPoissonRegressionModels ............3605 Example52.5:Random-EffectsModels ...................3614 Example52.6:ChangePointModels .....................3630 7 ?7 m& D/ u7 n" K" jExample52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 & V1 F- h0 [3 j4 @: xExample52.8:CoxModels ..........................3647 Example52.9:NormalRegressionwithIntervalCensoring .........3664 ! i2 |6 L5 b1 O& Z" a  IExample52.10:ConstrainedAnalysis ....................3666 Example52.11:ImplementaNewSamplingAlgorithm ...........3672 Example52.12:UsingaTransformationtoImproveMixing .........3683 Example52.13:Gelman-RubinDiagnostics .................3693 9 P/ Y" Q! h2 |, s! g: D! d) K% vReferences ...................................... 3700 游客，如果您要查看本帖隐藏内容请回复

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