model
{
	Tau.noninformative <- 1.0E-3
	Hyper.gamma <- 1.0E-2
	Tau.dummy <- 2.0E+4

	# observation
	for (i in 1:N.sample) {
		O[i] ~ dpois(nobs[i])
		nobs[i] <- exp(n[i])
	}

	# time change of density
	dg[1] ~ dnorm(mean.dg[3], tau) # dummy
	dg[2] ~ dnorm(mean.dg[3], tau) # dummy
	mean.g[1] <- mean.g[2]
	mean.g[2] <- mean.g[3]
	g[1] ~ dnorm(mean.g[1], tau)
	g[2] ~ dnorm(mean.g[2], tau)
	n1 ~ dnorm(0.0, Tau.noninformative) # initial n
	n[1] <- n1
	n[2] <- n1 + g[2]
	for (j in 3:N.year) {
		mean.dg[j] <- g[j - 1] - g[j - 2]
		dg[j] ~ dnorm(mean.dg[j], tau)
		mean.g[j] <- g[j - 1] + dg[j]
		g[j] ~ dnorm(mean.g[j], Tau.dummy)
		mean.n[j] <- n[j - 1] + g[j]
		n[j] ~ dnorm(mean.n[j], Tau.dummy)
	}

	# priors and hyper-priors
	tau ~ dgamma(Hyper.gamma, Hyper.gamma)
}