model {
for (i in 1:20) {Y[i, 1:4] ~ dmnorm(mu[], Sigma.inv[,])}
for (j in 1:4) {mu[j] <- alpha + beta*x[j]} alpha ~ dnorm(0, 0.0001) beta ~ dnorm(0, 0.0001) Sigma.inv[1:4, 1:4] ~ dwish(R[,], 4) Sigma[1:4, 1:4] <- inverse(Sigma.inv[,]) } Inits:
list(alpha = 40, beta = 1)

Data:
list(Y = structure(
.Data = c(47.8, 48.8, 49.0, 49.7,
46.4, 47.3, 47.7, 48.4,
46.3, 46.8, 47.8, 48.5,
45.1, 45.3, 46.1, 47.2,
47.6, 48.5, 48.9, 49.3,
52.5, 53.2, 53.3, 53.7,
51.2, 53.0, 54.3, 54.5,
49.8, 50.0, 50.3, 52.7,
48.1, 50.8, 52.3, 54.4,
45.0, 47.0, 47.3, 48.3,
51.2, 51.4, 51.6, 51.9,
48.5, 49.2, 53.0, 55.5,
52.1, 52.8, 53.7, 55.0,
48.2, 48.9, 49.3, 49.8,
49.6, 50.4, 51.2, 51.8,
50.7, 51.7, 52.7, 53.3,
47.2, 47.7, 48.4, 49.5,
53.3, 54.6, 55.1, 55.3,
46.2, 47.5, 48.1, 48.4,
46.3, 47.6, 51.3, 51.8),
.Dim = c(20, 4)),
x = c(8.0, 8.5, 9.0, 9.5),
R = structure(
.Data = c(4, 0, 0, 0,
0, 4, 0, 0,
0, 0, 4, 0,
0, 0, 0, 4),
.Dim = c(4, 4)))

   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   Sigma[1,1]   6.861   2.404   0.01689   3.626   6.393   12.85   1001   20000
   Sigma[1,2]   6.497   2.356   0.01643   3.319   6.045   12.37   1001   20000
   Sigma[1,3]   6.071   2.329   0.01634   2.929   5.614   11.89   1001   20000
   Sigma[1,4]   5.833   2.343   0.01607   2.632   5.379   11.62   1001   20000
   Sigma[2,1]   6.497   2.356   0.01643   3.319   6.045   12.37   1001   20000
   Sigma[2,2]   6.973   2.456   0.01719   3.688   6.491   13.11   1001   20000
   Sigma[2,3]   6.47   2.414   0.01734   3.207   5.985   12.45   1001   20000
   Sigma[2,4]   6.247   2.43   0.01711   2.943   5.771   12.34   1001   20000
   Sigma[3,1]   6.071   2.329   0.01634   2.929   5.614   11.89   1001   20000
   Sigma[3,2]   6.47   2.414   0.01734   3.207   5.985   12.45   1001   20000
   Sigma[3,3]   7.47   2.628   0.01968   3.931   6.95   14.08   1001   20000
   Sigma[3,4]   7.308   2.653   0.01951   3.716   6.781   13.97   1001   20000
   Sigma[4,1]   5.833   2.343   0.01607   2.632   5.379   11.62   1001   20000
   Sigma[4,2]   6.247   2.43   0.01711   2.943   5.771   12.34   1001   20000
   Sigma[4,3]   7.308   2.653   0.01951   3.716   6.781   13.97   1001   20000
   Sigma[4,4]   8.079   2.839   0.02058   4.221   7.527   15.23   1001   20000
   alpha   33.72   2.204   0.08798   29.41   33.71   38.02   1001   20000
   beta   1.869   0.2515   0.01004   1.379   1.868   2.362   1001   20000
   mu[1]   48.67   0.5575   0.008297   47.55   48.67   49.77   1001   20000
   mu[2]   49.6   0.5435   0.004251   48.51   49.6   50.67   1001   20000
   mu[3]   50.54   0.5583   0.004212   49.42   50.54   51.62   1001   20000
   mu[4]   51.47   0.5997   0.008236   50.28   51.47   52.64   1001   20000