I am assuming that you would like an AR(II) process term to account for serial autocorrelation.
Here is example WinBUGS code for running a Simultaneous Autoregressive model with an AR(II) term that could be adapted for your problem.
model;
{
# Defines likelihood
for(i in 1:I){ D[i, 2:(I+2)] ~ dmulti(C[i,], D[i, 1]); }
for(i in 1:(I-1)){
lphi[i] <- log(phi[i])
logit(phi[i]) <- beta + e[i]
for(j in (i+1):I){
C[i, j] <- lambda[j]*exp(sum(lphi[i:(j-1)]))
}
for (j in 1:i){
C[i+1, j] <- 0
}
}
for(i in 1:I){
C[i, i] <- lambda[i]
C[i, I+1] <- 1 - sum(C[i, 1:I])
}
# Defines epsilon
e[1] ~ dnorm(mu[1], tau1)
e[2] ~ dnorm(mu[2], tau2)
mu[1] <- 0
mu[2] <- (rho[1]/(1-rho[2]))*e[1]
tau1 <- ((1+rho[2])/(1-rho[2]))*((1-rho[2])*(1-rho[2]) - rho[1]*rho[1])*tau
tau2 <- tau*(1 - rho[2]*rho[2])
for(i in 3:(I-1)){
e[i] ~ dnorm(mu[i],tau)
mu[i] <- rho[1]*e[i-1] + rho[2]*e[i-2]
}
sigma <- 1/sqrt(tau)
# Prior distribution
for(i in 1:2){beta[i] ~ dnorm(0, 0.01)}
beta ~ dnorm(0, 0.01)
for(i in 1:I){lambda[i] ~ dunif(0, 1)}
tau ~ dgamma(0.001, 0.001)
## Prior for rho is approx (uniform on the AR(II) stationary triangle)
rho[1] ~ dunif(l, u)
u <- abs(1 - rho[2])
l <- -u
rho[2] ~ dunif(-1, 1)
}