Half-Cauchy population distribution for standard deviations...
model {
for (i in 1:10) {
for (j in offset[i]:(offset[i+1]-1)) {
y[j] ~ dnorm(psi[j], inv.sigma.squared[i])
psi[j] <- D*exp(-CL[i]*time[j]/V[i])/V[i]
}
CL[i] <- exp(theta[i, 1])
V[i] <- exp(theta[i, 2])
theta[i, 1:2] ~ dmnorm(mu.theta[], inv.Omega[,])
sigma[i] <- abs(z[i])/sqrt(gamma[i])
z[i] ~ dnorm(0, inv.B.squared)
gamma[i] ~ dgamma(0.5, 0.5)
inv.sigma.squared[i] <- 1/pow(sigma[i], 2)
}
inv.B.squared <- 1/pow(B, 2)
B ~ dunif(0, 100)
mu.theta[1:2] ~ dmnorm(m[], T[,])
inv.Omega[1:2, 1:2] ~ dwish(R[,], k)
Omega[1:2, 1:2] <- inverse(inv.Omega[,])
}

Inits:
list(
B = 0.1,
z = c(0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1),
gamma = c(1,1,1,1,1,1,1,1,1,1),
theta = structure(
.Data = c(
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201),
.Dim = c(10, 2)),
mu.theta = c(1.064710737,   2.708050201),
inv.Omega = structure(
.Data = c(
25.33071788,   0.0,
0.0,   25.33071788),
.Dim = c(2, 2)))

Data:
list(
D = 30,
y = c(
1.09,   0.75,   0.53,   0.34,   0.23,   0.02,
2.03,   1.28,   1.2,   1.02,   0.83,   0.28,
1.44,   1.3,   0.95,   0.68,   0.52,   0.06,
1.55,   0.96,   0.8,   0.62,   0.46,   0.08,
1.35,   0.78,   0.5,   0.33,   0.18,   0.02,
1.08,   0.59,   0.37,   0.23,   0.17,   0.0,
1.32,   0.74,   0.46,   0.28,   0.27,   0.03,
0.02,   0.0,   1.63,   1.01,   0.73,   0.55,
0.41,   0.01,   0.06,   0.02,   1.26,   0.73,
0.4,   0.3,   0.21,   0.0,   1.3,   0.7,
0.4,   0.25,   0.14,   0.0),
offset = c(1,   7,   13,   19,   25,   31,   37,   45,   53,   59,   65),
time = c(
2.0,   4.0,   6.0,   8.0,   10.0,   24.0,
2.0,   4.0,   6.0,   8.0,   10.0,   24.0,
2.0,   4.0,   6.0,   8.0,   10.0,   24.0,
2.0,   4.0,   6.0,   8.0,   10.0,   24.0,
2.0,   4.0,   6.0,   8.0,   10.0,   24.0,
2.0,   4.0,   6.0,   8.0,   10.0,   24.0,
2.0,   4.0,   6.0,   8.0,   10.0,   24.0,
28.0,   32.0,   2.0,   4.0,   6.0,   8.0,
10.0,   24.0,   28.0,   32.0,   2.0,   4.0,
6.0,   8.0,   10.0,   24.0,   2.0,   4.0,
6.0,   8.0,   10.0,   24.0),
m = c(1.064710737,   2.708050201),
T = structure(
.Data = c(
1.0E-4,   0.0,
0.0,   1.0E-4),
.Dim = c(2, 2)),
R = structure(
.Data = c(
0.08,   0.0,
0.0,   0.08),
.Dim = c(2, 2)),
k = 2.0)

   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   B   0.05963   0.0261   0.001209   0.02445   0.05482   0.1223   4001   10000
   Omega[1,1]   0.1456   0.08493   0.001125   0.05295   0.1257   0.3672   4001   10000
   Omega[1,2]   0.01737   0.02722   4.092E-4   -0.02687   0.01392   0.08125   4001   10000
   Omega[2,1]   0.01737   0.02722   4.092E-4   -0.02687   0.01392   0.08125   4001   10000
   Omega[2,2]   0.02803   0.01713   2.426E-4   0.01013   0.0237   0.07106   4001   10000
   mu.theta[1]   1.063   0.121   0.001284   0.8211   1.064   1.299   4001   10000
   mu.theta[2]   2.681   0.05737   8.368E-4   2.564   2.681   2.794   4001   10000
   sigma[1]   0.01746   0.009934   3.385E-4   0.007621   0.01474   0.04441   4001   10000
   sigma[2]   0.1729   0.07763   0.00184   0.08827   0.1538   0.3749   4001   10000
   sigma[3]   0.09103   0.03785   8.047E-4   0.04667   0.08188   0.188   4001   10000
   sigma[4]   0.08641   0.03409   5.681E-4   0.04597   0.07877   0.1723   4001   10000
   sigma[5]   0.03198   0.01504   3.929E-4   0.01542   0.02844   0.07016   4001   10000
   sigma[6]   0.03808   0.01724   4.756E-4   0.01847   0.03375   0.08201   4001   10000
   sigma[7]   0.05226   0.01705   3.636E-4   0.02997   0.04875   0.09471   4001   10000
   sigma[8]   0.058   0.01915   4.001E-4   0.03344   0.05394   0.1066   4001   10000
   sigma[9]   0.0477   0.02047   4.759E-4   0.02388   0.04285   0.1017   4001   10000
   sigma[10]   0.02581   0.01365   4.568E-4   0.0118   0.02225   0.06013   4001   10000

[example-10_4_1-cadralazine0]
Log-normal population distribution for standard deviations...
model {
for (i in 1:10) {
for (j in offset[i]:(offset[i+1]-1)) {
y[j] ~ dnorm(psi[j], inv.sigma.squared[i])
psi[j] <- D*exp(-CL[i]*time[j]/V[i])/V[i]
}
CL[i] <- exp(theta[i, 1])
V[i] <- exp(theta[i, 2])
theta[i, 1:2] ~ dmnorm(mu.theta[], inv.Omega[,])
log.sigma[i] ~ dnorm(mu.sigma, inv.omega.sigma.squared)
log(sigma[i]) <- log.sigma[i]
inv.sigma.squared[i] <- 1/pow(sigma[i], 2)
}
mu.sigma ~ dnorm(0, 0.0001)
med.sigma <- exp(mu.sigma)
omega.sigma ~ dunif(0, 100)
inv.omega.sigma.squared <- 1 / pow(omega.sigma, 2)
mu.theta[1:2] ~ dmnorm(m[], T[,])
inv.Omega[1:2, 1:2] ~ dwish(R[,], k)
Omega[1:2, 1:2] <- inverse(inv.Omega[,])
}

Inits:
list(
log.sigma = c(-2,-2,-2,-2,-2,-2,-2,-2,-2,-2),
mu.sigma = -2,
omega.sigma = 0.1,
theta = structure(
.Data = c(
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201,
1.064710737,   2.708050201),
.Dim = c(10, 2)),
mu.theta = c(1.064710737,   2.708050201),
inv.Omega = structure(
.Data = c(
25.33071788,   0.0,
0.0,   25.33071788),
.Dim = c(2, 2)))

Data:
list(
D = 30,
y = c(
1.09,   0.75,   0.53,   0.34,   0.23,   0.02,
2.03,   1.28,   1.2,   1.02,   0.83,   0.28,
1.44,   1.3,   0.95,   0.68,   0.52,   0.06,
1.55,   0.96,   0.8,   0.62,   0.46,   0.08,
1.35,   0.78,   0.5,   0.33,   0.18,   0.02,
1.08,   0.59,   0.37,   0.23,   0.17,   0.0,
1.32,   0.74,   0.46,   0.28,   0.27,   0.03,
0.02,   0.0,   1.63,   1.01,   0.73,   0.55,
0.41,   0.01,   0.06,   0.02,   1.26,   0.73,
0.4,   0.3,   0.21,   0.0,   1.3,   0.7,
0.4,   0.25,   0.14,   0.0),
offset = c(1,   7,   13,   19,   25,   31,   37,   45,   53,   59,   65),
time = c(
2.0,   4.0,   6.0,   8.0,   10.0,   24.0,
2.0,   4.0,   6.0,   8.0,   10.0,   24.0,
2.0,   4.0,   6.0,   8.0,   10.0,   24.0,
2.0,   4.0,   6.0,   8.0,   10.0,   24.0,
2.0,   4.0,   6.0,   8.0,   10.0,   24.0,
2.0,   4.0,   6.0,   8.0,   10.0,   24.0,
2.0,   4.0,   6.0,   8.0,   10.0,   24.0,
28.0,   32.0,   2.0,   4.0,   6.0,   8.0,
10.0,   24.0,   28.0,   32.0,   2.0,   4.0,
6.0,   8.0,   10.0,   24.0,   2.0,   4.0,
6.0,   8.0,   10.0,   24.0),
m = c(1.064710737,   2.708050201),
T = structure(
.Data = c(
1.0E-4,   0.0,
0.0,   1.0E-4),
.Dim = c(2, 2)),
R = structure(
.Data = c(
0.08,   0.0,
0.0,   0.08),
.Dim = c(2, 2)),
k = 2.0)

   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   Omega[1,1]   0.1511   0.08737   0.001159   0.05688   0.1285   0.3882   4001   10000
   Omega[1,2]   0.01709   0.02689   4.377E-4   -0.02746   0.01413   0.07985   4001   10000
   Omega[2,1]   0.01709   0.02689   4.377E-4   -0.02746   0.01413   0.07985   4001   10000
   Omega[2,2]   0.02723   0.01609   2.11E-4   0.009814   0.02312   0.06801   4001   10000
   med.sigma   0.04825   0.01344   2.822E-4   0.02629   0.04683   0.07868   4001   10000
   mu.sigma   -3.069   0.2751   0.005989   -3.639   -3.061   -2.542   4001   10000
   mu.theta[1]   1.061   0.1251   0.001298   0.8044   1.061   1.31   4001   10000
   mu.theta[2]   2.684   0.05565   8.193E-4   2.572   2.684   2.793   4001   10000
   omega.sigma   0.7455   0.2769   0.008735   0.3354   0.7023   1.386   4001   10000
   sigma[1]   0.02228   0.01237   6.062E-4   0.008849   0.01862   0.05339   4001   10000
   sigma[2]   0.1359   0.04667   0.001781   0.07818   0.1262   0.2557   4001   10000
   sigma[3]   0.07937   0.02785   8.88E-4   0.04446   0.07327   0.1496   4001   10000
   sigma[4]   0.07706   0.0258   7.437E-4   0.04368   0.07139   0.1435   4001   10000
   sigma[5]   0.03483   0.0152   5.929E-4   0.01652   0.0311   0.07385   4001   10000
   sigma[6]   0.038   0.01494   4.896E-4   0.01941   0.03522   0.07479   4001   10000
   sigma[7]   0.05098   0.01556   4.476E-4   0.03083   0.04819   0.08736   4001   10000
   sigma[8]   0.0552   0.01558   4.655E-4   0.03328   0.05214   0.09341   4001   10000
   sigma[9]   0.04546   0.01596   4.755E-4   0.02441   0.0427   0.08433   4001   10000
   sigma[10]   0.02899   0.01374   5.145E-4   0.01243   0.02611   0.06437   4001   10000

[example-10_4_1-cadralazine1]