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

建不了的模。

Chapter 52 " U* f1 k9 Y$ X8 uTheMCMCProcedure ) D" g: Z. C: j! S& o1 dContents Overview:MCMCProcedure ............................ 3478 ) V3 ]) |0 V) x& j" {% RPROCMCMCComparedwithOtherSASProcedures ............3479 GettingStarted:MCMCProcedure .......................... 3479   N% u4 P4 \$ Y# \& g) l; f- ^SimpleLinearRegression ...........................3480 TheBehrens-FisherProblem ..........................3488 / d* j/ u. t# N& U) q9 T( O9 F7 A! UMixed-EffectsModel .............................3492 Syntax:MCMCProcedure .............................. 3495 PROCMCMCStatement ...........................3496 3 a4 a& f) G' D, X2 G) w2 K% WARRAYStatement ...............................3508 , i# C5 R% \# ?BEGINCNST/ENDCNSTStatement .....................3509 BEGINNODATA/ENDNODATAStatements .................3511 # {) w1 f* g. Y5 GBYStatement .................................3511 MODELStatement ...............................3512 0 _4 O, i6 t2 t" C) S# oPARMSStatement ...............................3515 PRIOR/HYPERPRIORStatement .......................3516 ProgrammingStatements ...........................3516 ; T% h: ~" P. [% S/ a( t/ fUDSStatement .................................3518 . |: U8 C! [& F2 A( \Details:MCMCProcedure .............................. 3522 9 r/ ?( v0 \6 X6 w1 r; e& `HowPROCMCMCWorks ..........................3522 BlockingofParameters ............................3523 $ }. @; h4 t0 d$ A; cSamplers ....................................3524 TuningtheProposalDistribution .......................3525 InitialValuesoftheMarkovChains ......................3528 AssignmentsofParameters ..........................3528 StandardDistributions .............................3530 1 s7 w+ J+ C' u- s8 VSpecifyingaNewDistribution .........................3541 UsingDensityFunctionsintheProgrammingStatements ...........3542 TruncationandCensoring ...........................3544 ' n2 d' T2 n( t( ?( O5 l2 NMultivariateDensityFunctions ........................3546 SomeUsefulSASFunctions ..........................3549 MatrixFunctionsinPROCMCMC ......................3551 ModelingJointLikelihood ...........................3556 - ~* _; ^( Y4 A/ m( sRegeneratingDiagnosticsPlots ........................3557 PosteriorPredictiveDistribution ........................3560 ( @) s+ f# ^/ \' e8 Q5 ^4 mHandlingofMissingData ...........................3565 FloatingPointErrorsandOverflows ......................3565 HandlingErrorMessages ...........................3568 # E, U, P( E4 g! e' u+ p' x" f: _ComputationalResources ...........................3570 DisplayedOutput ................................3571 4 e: @$ y/ }6 q) R6 RODSTableNames ...............................3575 & }4 f& ~# b+ \$ U8 d" _ODSGraphics .................................3577 Examples:MCMCProcedure ............................ 3578 Example52.1:SimulatingSamplesFromaKnownDensity .........3578 : _# m6 m4 \0 M! W* r6 R+ P6 D5 wExample52.2:Box-CoxTransformation ...................3583 5 s$ k$ f- t) ^% Y) ?6 xExample52.3:GeneralizedLinearModels ..................3592 + _* V# K: |, e$ C* n- F3 ^Example52.4:NonlinearPoissonRegressionModels ............3605 + x$ y( n) K1 S0 }# l, u# k+ RExample52.5:Random-EffectsModels ...................3614   Z+ u' h* A, b% c! m  O* BExample52.6:ChangePointModels .....................3630 0 S) A. r8 r6 ]  kExample52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 ' o  B: j8 k/ Q* XExample52.8:CoxModels ..........................3647 Example52.9:NormalRegressionwithIntervalCensoring .........3664 Example52.10:ConstrainedAnalysis ....................3666   ]: s1 M% [; X& p+ t0 e5 rExample52.11:ImplementaNewSamplingAlgorithm ...........3672 Example52.12:UsingaTransformationtoImproveMixing .........3683 Example52.13:Gelman-RubinDiagnostics .................3693 References ...................................... 3700 . `! I/ r: X1 V 游客，如果您要查看本帖隐藏内容请回复

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