model {
	for (i in 1:k) {
		w[i] <- 1/vars[i]
		r[i] ~ dnorm(mu,w[i])
	}

	#Calculate Cochran Q
	Q <- sum(w*(r-mu)**2)

	#Calculate I2
	dfr <- k-1
	Isq <- max(0,(Q-dfr)/Q)


	#prior distributions
	mu ~ dnorm(hp.mu.mean,hp.mu.prec)

 # predictive distribution
 theta.new <- mu

}
