model 
{
	# Model
	# =====

	# Likelihood
	for (i in 1 : N) {
		Y[i]  ~ dnorm(mu[i],tau)
		mu[i] <- inprod(X[i,],beta[]) 
	}

	# prior on precision
	tau  ~ dgamma(0.5, 0.0005)		# precision
	sigma2 <- 1/tau				# variance
	sigma <- sqrt(1 / tau)			# standard deviation

        for (i in 1 : P){
         ##  beta[i] ~ dnorm(0,lambda) 		## ridge
            beta[i] ~ ddexp(0,lambda)		## lasso
        }
        lambda ~ dgamma(0.1,0.1)

}