model
{
	Tau.noninformative <- 1.0E-3
	Hyper.gamma <- 1.0E-2
	Tau.noise <- 5.0E+4

	# observation
	for (i in 1:N.sample) {
		Landing[i] ~ dpois(landing[i])
		landing[i] <- exp(log.landing[i])
		log.landing[i] ~ dnorm(m[i], tau[1]) # shore noize
		m[i] <- x[LV[i]] + a.year[Year[i]]
	}

	# time change of density
	# LV[i]: pointer to lataent variable
	for (i in 1:N.sample) {
		x[LV[i]] ~ dnorm(mean.x[i], Tau.noise)
		mean.x[i] <- x[LV[i] - 1] + r[LV[i]]
		r[LV[i]] ~ dnorm(mean.r[i], Tau.noise)
		mean.r[i] <- r[LV[i] - 1] + dr[LV[i]]
		dr[LV[i]] ~ dnorm(dr[LV[i] - 1], tau[2])
	}
	for (k in 1:N.LV.init) {
		x[LV.init[k]] ~ dnorm(0.0, Tau.noninformative)
		r[LV.init[k]] ~ dnorm(0.0, Tau.noninformative)
		dr[LV.init[k]] ~ dnorm(0.0, Tau.noninformative)
	}
	for (y in 1:N.year) {
		a.year[y] ~ dnorm(0.0, tau[3])
	}

	# priors and hyper-priors
	for (k in 1:N.tau) {
		tau[k] ~ dgamma(Hyper.gamma, Hyper.gamma)
	}
}