model{

	# Likelihood

	for(i in 1:n.cc){

	z[i] <- equals(reports.star[i], 0)	# I(reports = 0)

	logit(pi[i]) <- gamma[1]*intercept.cc[i] +
		gamma[2]*share[i] +
		gamma[3]*owner[i] +
		gamma[4]*accounts[i]

	mu[i] <- beta[1]*intercept.cc[i] +
		beta[2]*share[i] +
		beta[3]*owner[i] +
		beta[4]*accounts[i] +
		(1 - 2*tau)*w[i]/(tau*(1 - tau))

	prec[i] <- delta*tau*(1 - tau)/(2*w[i])

	log.like[i] <- z[i]*log(1 - pi[i]) + (1 - z[i])*(log(pi[i]) +
		0.5*log(prec[i]/(2*3.14159)) - 0.5*pow(reports.star[i] - mu[i], 2)*prec[i])

	#	zeros trick

	zeros[i] <- 0

	zeros[i] ~ dpois(lambda[i])

	lambda[i] <- -log.like[i] + 10000

	}

	# prior distributions

	for(i in 1:4){

	beta[i] ~ dnorm(0, 0.1)

	gamma[i] ~ dnorm(0, 0.1)
	}

	delta ~ dgamma(0.001, 0.001)


	for(i in 1:n.cc){w[i] ~ dexp(delta)}


}