model {
for(j in 1:N) {
for (i in 1:4) {
y[i,j] ~ dnorm(mu[i,j], tau[i])
}
mu[1,j] <- Linf[1] - (Linf[1] - L0[1])*exp(-K[1]*x[1,j])
mu[2,j] <- Linf[2] - (Linf[2] - L0[2])*exp(-K[2]*x[2,j])
mu[3,j] <- alpha[3] - beta[3]*pow(gamma[3], x[3,j])
mu[4,j] <- alpha[4] - beta[4]*pow(gamma[4], x[4,j])
}
L0[1] ~ dunif(0, 100)
L0[2] ~ dnorm(0, 0.0001)I(0, Linf[2])
Linf[1] <- L0[1] + beta[1]
Linf[2] ~ dnorm(0, 0.0001)I(L0[2], )
K[1] ~ dunif(0, 100)
K[2] ~ dunif(0, 100)
for (i in 1:2) {alpha[i] <- Linf[i]}
for (i in 3:4) {alpha[i] ~ dunif(0, 100)}
beta[1] ~ dunif(0, 100)
beta[2] <- Linf[2] - L0[2]
for (i in 3:4) {beta[i] ~ dunif(0, 100)}
for (i in 1:2) {gamma[i] <- exp(-K[i])}
gamma[3] ~ dunif(0, 1)
gamma[4] ~ dgamma(0.001, 0.001)I(0, 1)
for (i in 1:4) {
tau[i] <- 1/sigma2[i]
log(sigma2[i]) <- 2*log.sigma[i]
log.sigma[i] ~ dunif(-10, 10)
}
}

Inits:
list(alpha = c(NA, NA, 3, 3), beta = c(2, NA, 2, 2), gamma = c(NA, NA, 0.9, 0.9), K = c(0.1, 0.1), Linf = c(NA, 3), L0 = c(1, 1), log.sigma = c(-5, 0, -5, -5))

Data:
list(x = structure(
.Data = c(1.0, 1.5, 1.5, 1.5, 2.5, 4.0, 5.0, 5.0, 7.0,
8.0, 8.5, 9.0, 9.5, 9.5, 10.0, 12.0, 12.0, 13.0,
13.0, 14.5, 15.5, 15.5, 16.5, 17.0, 22.5, 29.0, 31.5,
1.0, 1.5, 1.5, 1.5, 2.5, 4.0, 5.0, 5.0, 7.0,
8.0, 8.5, 9.0, 9.5, 9.5, 10.0, 12.0, 12.0, 13.0,
13.0, 14.5, 15.5, 15.5, 16.5, 17.0, 22.5, 29.0, 31.5,
1.0, 1.5, 1.5, 1.5, 2.5, 4.0, 5.0, 5.0, 7.0,
8.0, 8.5, 9.0, 9.5, 9.5, 10.0, 12.0, 12.0, 13.0,
13.0, 14.5, 15.5, 15.5, 16.5, 17.0, 22.5, 29.0, 31.5,
1.0, 1.5, 1.5, 1.5, 2.5, 4.0, 5.0, 5.0, 7.0,
8.0, 8.5, 9.0, 9.5, 9.5, 10.0, 12.0, 12.0, 13.0,
13.0, 14.5, 15.5, 15.5, 16.5, 17.0, 22.5, 29.0, 31.5),
.Dim = c(4, 27)),
y = structure(
.Data = c(1.80, 1.85, 1.87, 1.77, 2.02, 2.27, 2.15, 2.26, 2.47,
2.19, 2.26, 2.40, 2.39, 2.41, 2.50, 2.32, 2.32, 2.43,
2.47, 2.56, 2.65, 2.47, 2.64, 2.56, 2.70, 2.72, 2.57,
1.80, 1.85, 1.87, 1.77, 2.02, 2.27, 2.15, 2.26, 2.47,
2.19, 2.26, 2.40, 2.39, 2.41, 2.50, 2.32, 2.32, 2.43,
2.47, 2.56, 2.65, 2.47, 2.64, 2.56, 2.70, 2.72, 2.57,
1.80, 1.85, 1.87, 1.77, 2.02, 2.27, 2.15, 2.26, 2.47,
2.19, 2.26, 2.40, 2.39, 2.41, 2.50, 2.32, 2.32, 2.43,
2.47, 2.56, 2.65, 2.47, 2.64, 2.56, 2.70, 2.72, 2.57,
1.80, 1.85, 1.87, 1.77, 2.02, 2.27, 2.15, 2.26, 2.47,
2.19, 2.26, 2.40, 2.39, 2.41, 2.50, 2.32, 2.32, 2.43,
2.47, 2.56, 2.65, 2.47, 2.64, 2.56, 2.70, 2.72, 2.57),
.Dim = c(4, 27)), N = 27)

   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   alpha[1]   2.65   0.07281   0.001407   2.527   2.644   2.809   10001   50000
   alpha[2]   2.651   0.07263   0.001245   2.529   2.644   2.814   10001   50000
   alpha[3]   2.656   0.07748   0.001929   2.532   2.647   2.829   10001   50000
   alpha[4]   2.654   0.07424   0.001737   2.528   2.647   2.819   10001   50000
   beta[1]   0.9751   0.07746   0.001512   0.8275   0.9736   1.129   10001   50000
   beta[2]   0.9747   0.07807   6.402E-4   0.8263   0.9733   1.129   10001   50000
   beta[3]   0.9759   0.07796   0.001022   0.828   0.9744   1.135   10001   50000
   beta[4]   0.9759   0.07727   9.129E-4   0.8288   0.9742   1.132   10001   50000
   gamma[1]   0.8607   0.03351   7.849E-4   0.7833   0.8646   0.9146   10001   50000
   gamma[2]   0.8613   0.03373   5.879E-4   0.7845   0.8651   0.9161   10001   50000
   gamma[3]   0.8632   0.03293   7.05E-4   0.7892   0.8665   0.9189   10001   50000
   gamma[4]   0.8623   0.03386   6.953E-4   0.7839   0.8662   0.917   10001   50000
   sigma2[1]   0.009987   0.003213   2.702E-5   0.005568   0.009403   0.01791   10001   50000
   sigma2[2]   0.009961   0.003191   2.136E-5   0.005532   0.009387   0.01774   10001   50000
   sigma2[3]   0.009973   0.003169   2.552E-5   0.005552   0.009384   0.01777   10001   50000
   sigma2[4]   0.009975   0.003194   2.505E-5   0.005582   0.009389   0.01786   10001   50000