model{
    for(i in 1:N){
       mu[i]<-b0+b1*x[i]
       y[i]~dt(mu[i],1/sigma^2,tau)
       # Posterior predictive 
       pred[i]~dt(mu[i],1/sigma^2,tau)
    }
    # Priors
    b0~dnorm(0,0.0001)
    b1~dnorm(0,0.0001)
    sigma~dunif(0.001,1000)
    tau<-ntau+1
    ntau~dexp(1/29)
}