model
{
	Tau.noninformative <- 1.0E-4
	Hyper.gamma <- 1.0E-2

	# observation
	for (i in 1:N.sample) {
		log.APC[i] ~ dnorm(xA[LV[i]], tau[1])
		log.LM[i] ~ dnorm(xL[LV[i]], tau[1])
	}

	# time change of density
	# LV[i]: pointer to lataent variable
	for (i in 1:N.sample) {
		xA[LV[i]] ~ dnorm(mean.xA[i], tau[2])
		xL[LV[i]] ~ dnorm(mean.xL[i], tau[2])
		mean.xA[i] <- xA[LV[i] - 1] + Delta.time[i] * (
			pA[1]
			+ pA[2] * (Temp[i] - Mean.temp)
			+ pA[3] * (xA[LV[i] - 1] - Mean.x)
			+ pA[4] * (xL[LV[i] - 1] - Mean.x)
		);
		mean.xL[i] <- xL[LV[i] - 1] + Delta.time[i] * (
			pL[1]
			+ pL[2] * (Temp[i] - Mean.temp)
			+ pL[3] * (xA[LV[i] - 1] - Mean.x)
			+ pL[4] * (xL[LV[i] - 1] - Mean.x)
		);
	}
	for (k in 1:N.LV.init) {
		xA[LV.init[k]] ~ dnorm(0.0, Tau.noninformative)
		xL[LV.init[k]] ~ dnorm(0.0, Tau.noninformative)
	}

	# priors and hyper-priors
	for (k in 1:N.p) {
		pA[k] ~ dnorm(0.0, Tau.noninformative);
		pL[k] ~ dnorm(0.0, Tau.noninformative);
	}
	for (k in 1:N.tau) {
		tau[k] ~ dgamma(Hyper.gamma, Hyper.gamma)
	}
}