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

建不了的模。

/ h% H" o+ n8 h! B  M- O# y

Chapter 52 TheMCMCProcedure Contents Overview:MCMCProcedure ............................ 3478 - \4 d  M- x4 Y9 j# P/ VPROCMCMCComparedwithOtherSASProcedures ............3479 GettingStarted:MCMCProcedure .......................... 3479 5 ~0 l( Q) F# k) Y6 rSimpleLinearRegression ...........................3480 TheBehrens-FisherProblem ..........................3488 0 y+ S5 y& J( }) d  i1 {* SMixed-EffectsModel .............................3492 2 s$ h2 G% Z" m/ o. }# sSyntax:MCMCProcedure .............................. 3495   Q. A- s9 i3 V4 e# |PROCMCMCStatement ...........................3496 ARRAYStatement ...............................3508 BEGINCNST/ENDCNSTStatement .....................3509 BEGINNODATA/ENDNODATAStatements .................3511 BYStatement .................................3511 5 F3 ?" U1 w) {MODELStatement ...............................3512 PARMSStatement ...............................3515 PRIOR/HYPERPRIORStatement .......................3516 ProgrammingStatements ...........................3516 * T) l7 l/ h$ t0 h: OUDSStatement .................................3518 4 r7 ^$ |4 S/ v8 {0 v, f8 d2 L& ?Details:MCMCProcedure .............................. 3522 HowPROCMCMCWorks ..........................3522 1 V3 ?% _" Q7 R) D$ {# XBlockingofParameters ............................3523 " G4 b- \4 D, a: Z+ B" o+ `Samplers ....................................3524 8 l- p$ j1 M, r+ TTuningtheProposalDistribution .......................3525 InitialValuesoftheMarkovChains ......................3528 9 h( Z' S6 g8 J' O( r  ], MAssignmentsofParameters ..........................3528 StandardDistributions .............................3530 SpecifyingaNewDistribution .........................3541 UsingDensityFunctionsintheProgrammingStatements ...........3542 TruncationandCensoring ...........................3544 8 T& F; k9 s1 p3 D7 JMultivariateDensityFunctions ........................3546 1 p6 Q& S& W; B; V- ^SomeUsefulSASFunctions ..........................3549 MatrixFunctionsinPROCMCMC ......................3551 + C$ p2 Z. s, P1 rModelingJointLikelihood ...........................3556 7 R2 j- k( ?9 f3 iRegeneratingDiagnosticsPlots ........................3557 PosteriorPredictiveDistribution ........................3560 ; e6 t% h! H& M. W/ g" F5 {HandlingofMissingData ...........................3565 2 }5 O9 h* c! ?FloatingPointErrorsandOverflows ......................3565 HandlingErrorMessages ...........................3568 ) R: d* e% n1 S+ G2 }5 j% ?% s" ^! s: ]ComputationalResources ...........................3570 DisplayedOutput ................................3571 ODSTableNames ...............................3575 ! _0 b8 `; O, B" [4 X! DODSGraphics .................................3577 1 A; P8 k! p$ m# cExamples:MCMCProcedure ............................ 3578 Example52.1:SimulatingSamplesFromaKnownDensity .........3578 8 Z. ?* u' K: u0 k$ j& d9 rExample52.2:Box-CoxTransformation ...................3583 7 n& f1 z' u) i% v2 E( u$ [9 RExample52.3:GeneralizedLinearModels ..................3592 ) Y/ q3 R. J4 q$ NExample52.4:NonlinearPoissonRegressionModels ............3605 Example52.5:Random-EffectsModels ...................3614 2 m1 c, |  G( N; J0 T2 hExample52.6:ChangePointModels .....................3630 Example52.7:ExponentialandWeibullSurvivalAnalysis ..........3634 ; Z: h  c, ~8 ZExample52.8:CoxModels ..........................3647 0 v4 j- t1 F1 a# D! `2 M+ gExample52.9:NormalRegressionwithIntervalCensoring .........3664 - x4 `  N2 k8 {7 d1 tExample52.10:ConstrainedAnalysis ....................3666 Example52.11:ImplementaNewSamplingAlgorithm ...........3672 7 E. w) j- \$ `) G) EExample52.12:UsingaTransformationtoImproveMixing .........3683 Example52.13:Gelman-RubinDiagnostics .................3693 ; L% w4 a, q6 L$ iReferences ...................................... 3700 $ d5 b3 ]2 c9 f. a) g 游客，如果您要查看本帖隐藏内容请回复

% A% f+ z0 Y) W0 Q6 p

tbob228

谢谢好人一枚

w785485068

支持了。。。。。。。

破碎的风

不错不错！
% z3 }3 U+ H  w+ u

本世界的大人

谢谢分享，学习学习。。。。