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

建不了的模。

Chapter 52 TheMCMCProcedure Contents Overview:MCMCProcedure ............................ 3478 * m- q( }- Z0 W3 E- XPROCMCMCComparedwithOtherSASProcedures ............3479 + E6 }4 W% e3 K" A! D. B5 H4 s6 C' ?GettingStarted:MCMCProcedure .......................... 3479 SimpleLinearRegression ...........................3480 9 B( j' g  ~$ f) g! O5 s! GTheBehrens-FisherProblem ..........................3488 ( q. `  g& K5 MMixed-EffectsModel .............................3492 9 ?7 \: v/ Q6 F7 \& lSyntax:MCMCProcedure .............................. 3495 # Y' K, C; H# s/ N! l- ~' PPROCMCMCStatement ...........................3496 ARRAYStatement ...............................3508 / b3 c: N# Y5 _' k+ V6 NBEGINCNST/ENDCNSTStatement .....................3509 : Z% v, t9 j/ [: n1 B' e6 ^BEGINNODATA/ENDNODATAStatements .................3511 , S7 y2 M4 G/ p8 lBYStatement .................................3511 MODELStatement ...............................3512 " T' X+ v! b+ |, \- Z5 F" APARMSStatement ...............................3515 # L" R; q6 B( ePRIOR/HYPERPRIORStatement .......................3516 7 a& K! C# M  t; j0 Y8 T+ fProgrammingStatements ...........................3516 UDSStatement .................................3518 Details:MCMCProcedure .............................. 3522 2 O) X7 s+ Z8 X! J, tHowPROCMCMCWorks ..........................3522 BlockingofParameters ............................3523 0 X1 `1 u" Z" B: A* @. V$ SSamplers ....................................3524 TuningtheProposalDistribution .......................3525   Y: E- k/ d! x3 O) NInitialValuesoftheMarkovChains ......................3528 , V0 k. W/ m4 ?$ z, f; J) [! hAssignmentsofParameters ..........................3528 StandardDistributions .............................3530 SpecifyingaNewDistribution .........................3541 $ i+ p5 G" a, ^* kUsingDensityFunctionsintheProgrammingStatements ...........3542 TruncationandCensoring ...........................3544 MultivariateDensityFunctions ........................3546 SomeUsefulSASFunctions ..........................3549 MatrixFunctionsinPROCMCMC ......................3551 7 A" l; P% ]! H- m" E" JModelingJointLikelihood ...........................3556 RegeneratingDiagnosticsPlots ........................3557 ; x( x  l7 W  Y4 W) K& nPosteriorPredictiveDistribution ........................3560 HandlingofMissingData ...........................3565 FloatingPointErrorsandOverflows ......................3565 7 V" x! M' u1 M  X3 \/ tHandlingErrorMessages ...........................3568 ComputationalResources ...........................3570 2 s' [3 n% D$ Q9 CDisplayedOutput ................................3571 ODSTableNames ...............................3575 - g$ A' Z+ r- R# G4 q- KODSGraphics .................................3577 Examples:MCMCProcedure ............................ 3578 Example52.1:SimulatingSamplesFromaKnownDensity .........3578 # k3 I2 @' \5 u7 @Example52.2:Box-CoxTransformation ...................3583 0 y8 A( P( j& j; }7 Y3 B& kExample52.3:GeneralizedLinearModels ..................3592 Example52.4:NonlinearPoissonRegressionModels ............3605 ! x, X9 T1 g2 L* X/ xExample52.5:Random-EffectsModels ...................3614 Example52.6:ChangePointModels .....................3630 ) M' \- z5 Z+ r8 ZExample52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 Example52.8:CoxModels ..........................3647 Example52.9:NormalRegressionwithIntervalCensoring .........3664 ) b  c, W, v* `, ^1 l" dExample52.10:ConstrainedAnalysis ....................3666 Example52.11:ImplementaNewSamplingAlgorithm ...........3672 & n6 f1 n  m- G, F. aExample52.12:UsingaTransformationtoImproveMixing .........3683 # D6 ?$ S7 R" a6 `1 {Example52.13:Gelman-RubinDiagnostics .................3693 References ...................................... 3700 ! a6 d& ~: F/ A# c( s 游客，如果您要查看本帖隐藏内容请回复

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