library(rjags)
mvn.data <- list(q=2,n=1)

m <- jags.model("/Users/dbmad/Documents/G6102/jags/mvn.bug", 
                data=mvn.data,								
                nchain=1
               )
m$update(1000)
parameters <- c("Prec.Y")
x <- jags.samples(m,parameters,n.iter=10000)

samples <- coda.samples(m, parameters, 10000)
summary(samples)
foo<-samples[[1]]

# convert each row to a correlation matrix
for (i in 1:dim(foo)[1]) {
	temp <- foo[i,]                # each row is a precision matrix
	dim(temp) <- c(2,2)
	temp <- solve(temp)            # invert to get a variance matrix
	temp[1,1] <- sqrt(temp[1,1])   # convert to an sd 
	temp[2,2] <- sqrt(temp[2,2])   # convert to an sd 
	temp[2,1] <- temp[2,1]/(temp[1,1]*temp[2,2])   # convert to a correlation 
	temp[1,2] <- temp[1,2]/(temp[1,1]*temp[2,2])   # convert to a correlation 
	foo[i,] <- c(temp[1,1],temp[2,1],temp[1,2],temp[2,2])
	}


par(mfrow=c(2,2))
plot(density(foo[,1]),main="1,1")
plot(density(foo[,2]),main="2,1")
plot(density(foo[,3]),main="1,2")
plot(density(foo[,4]),main="2,2")

#foo<-summary(x$Prec.Y,mean)$stat
#faa<-summary(x$Prec.Y,sd)$stat