我正在编写贝叶斯统计问题的WinBugs代码：
考虑以下模型，该模型考虑到VIX（第一个变量）提供SP500方差（第二个变量）的信息以及$ Y_t ^ S $和$ Y_t ^ V $可能相关的事实：登记/> 该模型位于http://i.stack.imgur.com/qMHdq.png
对于$ t = 1，\ ldots，200 $，其中$ \ rho $反映了$ Y_t ^ S $和$ Y_t ^ V $的增量之间的相互关系，$ \ alpha $是一个参数取值实线和$ N_2（M，V）$表示双变量正态分布，均值为$ M $，协方差矩阵为$ V $。
（问题是:) 为参数$ \ mu_s $，$ \ mu_v $，$ \ sigma $，$ \ omega $，$ \ rho $，$ \ alpha $指定合适的先验，并编写一个WinBugs脚本以使此模型适合您的数据。实施它以从该模型参数的后验分布中进行采样。
WinBugs代码是：
model{for(i in 1:200){
y[i+1,1:2] ~ dnorm(mean[i,1:2],tau[i,1:2,1:2])
mean[i,1] <- y[i,1]+mu[1]+alpha*exp(y[i,2])
mean[i,2]<- y[i,2]+mu[2]
tau[i,1,1]<-exp(y[i,2])/prec[1]
tau[i,1,2]<-exp(y[i,2]/2)*rho/sqrt(prec[1]*prec[2])
tau[i,2,1]<-exp(y[i,2]/2)*rho/sqrt(prec[1]*prec[2])
tau[i,2,2]<-(1/(prec[2]))
}
mu[1] ~ dnorm (0, 0.0001)
mu[2] ~ dnorm (0, 0.0001)
prec[1] ~ dgamma (0.001, 0.001)
prec[2] ~ dgamma (0.001, 0.001)
alpha~dnorm(1,10000)
rho~dnorm(0,10)
}
list(y =structure(.Data= c(3.291839303,3.296274588,3.295265738,3.297438773,3.298200053,3.298412011,3.296300932,3.296426043,3.294455203,3.294481658,3.285708048,3.284464574,3.287575569,3.283348727,3.283355512,3.280935583,3.285914948,3.287111684,3.286400327,3.289303491,3.291186746,3.29116009,3.294849647,3.297015994,3.298090756,3.299369994,3.298503754,3.300578094,3.301034339,3.301056053,3.300321518,3.301761166,3.301524809,3.301186314,3.3005194,3.302700982,3.301364274,3.298512491,3.300093081,3.300475917,3.297878641,3.297570124,3.300808449,3.301370783,3.303489809,3.303282476,3.299788312,3.297272339,3.300660688,3.293581304,3.297289862,3.296182373,3.294970773,3.289178542,3.289180774,3.294003026,3.29332277,3.286703413,3.294221453,3.285154331,3.280152517,3.272941046,3.273626206,3.27009395,3.270156904,3.27571666,3.279669225,3.28808818,3.284906505,3.290217199,3.293269718,3.292617095,3.29777145,3.297169381,3.299866701,3.304931922,3.30488027,3.303649561,3.306118232,3.307754826,3.307906605,3.309259582,3.309562037,3.309257451,3.309487508,3.309591846,3.309911091,3.312135025,3.311482607,3.312336061,3.314604473,3.315846543,3.31534678,3.316563686,3.315458122,3.312482018,3.315245917,3.316877848,3.316372983,3.317095535,3.31393257,3.313829271,3.30666945,3.308634834,3.301535654,3.298772321,3.295069851,3.303820042,3.314126455,3.316106697,3.317758387,3.318516185,3.318455693,3.319890621,3.320264714,3.318136407,3.313635254,3.313487574,3.30547605,3.30159638,3.306618004,3.314318146,3.31065296,3.307123626,3.306002323,3.303470376,3.299435382,3.305226653,3.305899267,3.30794935,3.314530804,3.312139259,3.313253293,3.307399755,3.301498781,3.305620033,3.299940723,3.305534079,3.311760217,3.309951512,3.314398169,3.312911143,3.311062677,3.315674421,3.315661824,3.319830321,3.321596359,3.322289603,3.322153111,3.321691617,3.324344199,3.324212469,3.325408924,3.325076221,3.32443474,3.32314893,3.325800858,3.323825279,3.321915182,3.322434321,3.316234618,3.317944305,3.310514886,3.309681258,3.315119807,3.312473558,3.31831173,3.31686738,3.322115879,3.319994568,3.323891208,3.323132421,3.320457869,3.314088528,3.313054794,3.314082206,3.319364268,3.315527433,3.31380186,3.315332072,3.318192769,3.317296379,3.318459865,3.320391417,3.322645108,3.320650938,3.321358125,3.323588265,3.323250037,3.318309644,3.32230201,3.321658486,3.323862366,3.324885109,3.325862386,3.324060105,3.325261087,3.323633617,3.319212277,3.323930349,3.325205636,-1.674871187,-1.837305384,-1.784901741,-1.824437164,-1.877095042,-1.853296595,-1.793076756,-1.802020721,-1.75360385,-1.750339701,-1.541660595,-1.537570704,-1.640896418,-1.545769835,-1.571902641,-1.556650006,-1.604336613,-1.6935902,-1.699715676,-1.778820579,-1.811756808,-1.762148494,-1.818778584,-1.826568672,-1.857709419,-1.859185357,-1.880873164,-1.863628277,-1.868840571,-1.857709419,-1.838025906,-1.843086364,-1.823727823,-1.815963058,-1.796505852,-1.835147398,-1.795132589,-1.739332463,-1.780168274,-1.785580061,-1.751643889,-1.700330607,-1.790343193,-1.795818949,-1.839468745,-1.833711714,-1.727193104,-1.651880385,-1.754258154,-1.611526503,-1.656547093,-1.59284645,-1.575092078,-1.5540471,-1.583117287,-1.674274013,-1.621581021,-1.528943106,-1.641471071,-1.453534332,-1.345690975,-1.216718593,-1.28451135,-1.161741385,-1.197198918,-1.315549541,-1.462376193,-1.587427911,-1.495750895,-1.563454293,-1.585808919,-1.589591272,-1.683878412,-1.639174734,-1.676066767,-1.705884658,-1.663594506,-1.654210604,-1.6972603,-1.728462971,-1.76413233,-1.79444677,-1.777474973,-1.770778032,-1.720871468,-1.751643889,-1.708364571,-1.716473539,-1.710229163,-1.73420046,-1.778820579,-1.79788129,-1.823727823,-1.83658546,-1.750339701,-1.689935542,-1.782193745,-1.808267093,-1.814558711,-1.854765047,-1.694811844,-1.654210604,-1.464249161,-1.394472583,-1.352258787,-1.379888524,-1.255280835,-1.422607479,-1.548864573,-1.565558689,-1.633460313,-1.659476569,-1.685086464,-1.677263996,-1.644350056,-1.596113873,-1.433397543,-1.499648104,-1.401421332,-1.350612172,-1.428435452,-1.538591373,-1.511445758,-1.415487857,-1.373953779,-1.335931446,-1.299891813,-1.357631945,-1.402730434,-1.449377291,-1.570312304,-1.556650006,-1.618216566,-1.527933706,-1.379038217,-1.453534332,-1.356803139,-1.423054399,-1.522402875,-1.47367507,-1.54680019,-1.524410013,-1.463312172,-1.527429445,-1.541148304,-1.628349281,-1.665956408,-1.602685826,-1.622143032,-1.631185029,-1.689327925,-1.67367725,-1.727193104,-1.71772782,-1.71334574,-1.749688341,-1.769444817,-1.716473539,-1.6935902,-1.705265784,-1.636312824,-1.644350056,-1.555087327,-1.545769835,-1.623831253,-1.591760035,-1.613194194,-1.610416485,-1.709607188,-1.703411805,-1.770778032,-1.745142444,-1.731645785,-1.622705408,-1.602685826,-1.643773495,-1.676665175,-1.631185029,-1.641471071,-1.667139772,-1.663005033,-1.660651132,-1.708985657,-1.766120707,-1.800638718,-1.711474452,-1.728462971,-1.782869953,-1.79925891,-1.714595509,-1.752296718,-1.755568243,-1.791708899,-1.807570829,-1.820896234,-1.76413233,-1.812456437,-1.746438846,-1.674274013,-1.792392558,-1.782193745),
.Dim=c(201,2))
)
list( mu=c(0,0), prec=c(1,1),alpha=1,rhi=0.5)
我收到错误＆＃34;期望多变量节点＆＃34;在编译模型时。代码有什么问题？
答案 0 :(得分：0)
您不能在dnorm中添加多个均值和差异，这是您目前正在进行的。模型期望您的似然函数是多变量的，但是您给它一个单变量似然函数。您指定的模型实际上是多元法线，在JAGS中您可以指定为dmnorm，它可以采用均值向量，然后是方差协方差矩阵（您已经指定）。尝试将模型顶部的dnorm更改为dmnorm，然后您应该好好去。