model
{
	# likelihood
	for (i in 1:N.sample) {
		LogCFU[i] ~ dnorm(mean[i], tau.error)
		mean[i] <- (
			x0[Line[i]] * xmax[Line[i]] * exp(mu[Line[i]] * Time[i])
			/ (xmax[Line[i]] + x0[Line[i]] * (exp(mu[Line[i]] * Time[i]) - 1))
		)
	}
	# fixed effects
	for (j in 1:N.line) {
		mu[j] <- p1 * exp(p2 * Temp[j])
	}
	p1 ~ dnorm(0.0, Tau.noninformative)
	p2 ~ dnorm(0.0, Tau.noninformative)

	# random effects
	for (j in 1:N.line) {
		x0[j] ~ dnorm(0.0, tau.x0)
		xmax[j] ~ dnorm(0.0, tau.xmax)
	}


	# hyper priors
	tau.x0 ~ dgamma(Hyper.gamma, Hyper.gamma)
	tau.xmax ~ dgamma(Hyper.gamma, Hyper.gamma)

	# ``measurement'' error
	tau.error ~ dgamma(Error.gamma, Error.gamma)
}