- 在线时间
- 1084 小时
- 最后登录
- 2015-9-10
- 注册时间
- 2014-4-18
- 听众数
- 162
- 收听数
- 1
- 能力
- 10 分
- 体力
- 43980 点
- 威望
- 6 点
- 阅读权限
- 255
- 积分
- 15251
- 相册
- 0
- 日志
- 0
- 记录
- 1
- 帖子
- 3471
- 主题
- 2620
- 精华
- 1
- 分享
- 0
- 好友
- 513
升级   0% TA的每日心情 | 开心 2015-3-12 15:35 |
|---|
签到天数: 207 天 [LV.7]常住居民III
 群组: 第六届国赛赛前冲刺培 群组: 国赛讨论 群组: 2014美赛讨论 群组: 2014研究生数学建模竞 群组: 数学中国试看培训视频 |
I/ l' N: L- M" z' u2 N* xChapter 52
- P/ h; }( D0 aTheMCMCProcedure2 J: p# G1 G/ e, q, t5 O2 s* i
Contents2 O3 W' q( d# l9 H
Overview:MCMCProcedure ............................ 3478
$ q" d0 L4 f% h7 o- F r9 Y$ J6 nPROCMCMCComparedwithOtherSASProcedures ............3479
. [5 p( m; m2 [* u9 F8 P* ~GettingStarted:MCMCProcedure .......................... 34797 v$ n( [4 g: Y( G% P, W
SimpleLinearRegression ...........................34809 U. s) d/ E' D% B* t: I- S3 [1 P1 T
TheBehrens-FisherProblem ..........................3488
5 G, [: S) C2 O* r5 \Mixed-EffectsModel .............................3492
8 h5 I! |9 _% J5 XSyntax:MCMCProcedure .............................. 34950 O4 A- e, h% L4 q* v d9 m
PROCMCMCStatement ...........................3496
) g/ p4 A7 ?/ A j, z% fARRAYStatement ...............................3508 X# k% x$ z+ V
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% a) Y0 A9 w) Q3 Q! _# I/ q
MODELStatement ...............................3512
0 b! S g, {( S- ~) L1 M& R2 MPARMSStatement ...............................35150 P: s. n$ Y( d( L6 m; Y
PRIOR/HYPERPRIORStatement .......................35165 @0 D3 ], N! o* q j: T0 I# K
ProgrammingStatements ...........................3516/ `. Z/ j+ z0 P) B; S# e
UDSStatement .................................3518
D% B6 [5 ]8 iDetails:MCMCProcedure .............................. 3522, K& W9 [3 A7 }- w: E
HowPROCMCMCWorks ..........................35220 \- U. B1 ]' ~: Y4 @! s0 g, I) b
BlockingofParameters ............................3523$ g _% B1 ]6 l7 S+ |' s
Samplers ....................................3524
1 o: Y2 H2 T( m, o# E0 u) [TuningtheProposalDistribution .......................35255 g6 o5 _/ n3 ^5 f2 m: p! N% Z T
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; g4 J2 `& [0 m" r
MultivariateDensityFunctions ........................3546
8 o& _; P' r1 n) KSomeUsefulSASFunctions ..........................35497 o' I2 S6 G* [8 _* P, g M
MatrixFunctionsinPROCMCMC ......................35514 p; d9 ~3 h" m5 I
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* A E) D4 z& h4 T; A$ a- M' a
ComputationalResources ...........................3570& ]" z/ R1 X9 ^% m. Y6 x' u- Z4 @4 C1 o
DisplayedOutput ................................3571
- v0 m/ b0 l8 d" E- n0 [ODSTableNames ...............................35756 o! A. l W% ?5 T& n4 i
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 ...................35832 ~7 P& F+ \$ W7 x \
Example52.3:GeneralizedLinearModels ..................3592
3 O1 b0 T* O4 p5 B, t3 BExample52.4:NonlinearPoissonRegressionModels ............3605, Q6 @8 j$ u, f$ ?' q( ]2 R
Example52.5:Random-EffectsModels ...................3614, m- r/ b$ a( ?0 i
Example52.6:ChangePointModels .....................3630
7 ?7 m& D/ u7 n" K" jExample52.7:ExponentialandWeibullSurvivalAnalysis ..........3634
& V1 F- h0 [3 j4 @: xExample52.8:CoxModels ..........................3647. v$ _: Z$ P$ Y2 D3 D
Example52.9:NormalRegressionwithIntervalCensoring .........3664
! i2 |6 L5 b1 O& Z" a IExample52.10:ConstrainedAnalysis ....................36664 y. T. U5 o5 q7 q
Example52.11:ImplementaNewSamplingAlgorithm ...........3672* n2 m5 Y, M" v7 R
Example52.12:UsingaTransformationtoImproveMixing .........36837 b1 |9 e5 p) O" n0 p" I8 T* G
Example52.13:Gelman-RubinDiagnostics .................3693
9 P/ Y" Q! h2 |, s! g: D! d) K% vReferences ...................................... 3700% [. o- p$ C! r. a3 A2 ?
|
$ M7 N9 D: l8 A6 M! {; {1 j' d |
zan
|