model{
    for(i in 1:N1){
       t[i,1:2]~dmnorm(mu,precI)
       for(j in 1:N2){
          K[i,j]~dnorm(t[i,j],lambdaerror[j])
       }
    }
    # Priors
    for(j in 1:N2){
       mu[j]~dnorm(0,0.001)
       lambdaerror[j]~dgamma(0.001,0.001)
       lambda[j]~dgamma(0.001,0.001)
    }
    precI<-inverse(prec)
    prec[1,1]<-1/lambda[1]
    prec[2,2]<-1/lambda[2]
    prec[1,2]<-rho/(pow(lambda[1],0.5)*pow(lambda[2],0.5))
    prec[2,1]<-rho/(pow(lambda[1],0.5)*pow(lambda[2],0.5))
    rho~dunif(-1,1)
}