bayesian-learning-with-R/model_file at master · NCSU-STATS/bayesian-learning-with-R · GitHub

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model{
  # Likelihood for theta[1,1:p]:
  #theta[1,1:p] ~ dmnorm(mu[1,1:p],precision1)

  #Prior for theta[1,1:p]
  #mu[1:1,p] ~ ?

  #for(i in 2:N)
  for(i in 2:N)
  {
    theta[i,1:p]   ~ dnorm(mu[i,1:p] ,precision2)
    mu[i,1:p] <- mu2[1:p] + rho * Y1[i-1,1:p]
  }

  # Prior for likelihood parameters: mu2, precision2, rho
  rho ~ dunif(-1,1)
  for(j in 1:p)
  {
    mu2[j] ~ dnorm(0,0.01)
  }

  precision2[1:p,1:p]~dwish(R[,],k)

  # Missing data model for Y1
  for(i in 1:N)
  {
    Y1[i,1:p]~dmnorm(x_mn[],x_prec[,])
  }

  # Priors for missing-data model parameters
  for(j in 1:p)
  {
    x_mn[j]~dnorm(0,0.01)
  }
  x_prec[1:p,1:p]~dwish(R[,],k)
  x_cov[1:p,1:p]<-inverse(x_prec[,])

  k<-p+0.1
  for(j1 in 1:p)
  {
    for(j2 in 1:p)
    {
      R[j1,j2]<-0.1*equals(j1,j2)
    }
  }

}