t distribution specified directly...
model {
for (i in 1:N) {
y[i] ~ dt(mu, tau ,4)
}
mu ~ dunif(-100, 100)
tau ~ dgamma(0.001, 0.001)
}

Inits:
list(mu = 0, tau = 1)

Data:
list(N=66, y=c(
28, 26, 33, 24, 34, -44, 27, 16, 40, -2,
29, 22, 24, 21, 25, 30, 23, 29, 31, 19,
24, 20, 36, 32, 36, 28, 25, 21, 28, 29,
37, 25, 28, 26, 30, 32, 36, 26, 30, 22,
36, 23, 27, 27, 28, 27, 31, 27, 26, 33,
26, 32, 32, 24, 39, 28, 24, 25, 32, 25,
29, 27, 28, 29, 16, 23))

   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   deviance   436.4   2.02   0.02387   434.4   435.8   441.8   1001   10000
   mu   27.48   0.6604   0.009961   26.2   27.47   28.78   1001   10000
   tau   0.04919   0.01196   1.829E-4   0.02968   0.04776   0.07635   1001   10000
   
[example-8_2_1-newcomb0][example-8_2_1-newcomb1][example-8_2_1-newcomb2]
t distribution specified indirectly...
model {
for (i in 1:N) {
y[i]
~ dnorm(mu, invsigma2[i])
invsigma2[i] <- tau*lambda[i]/4
lambda[i] ~ dchisqr(4)
}
mu ~ dunif(-100, 100)
tau ~ dgamma(0.001, 0.001)
}

Inits:
list(mu = 0, tau = 1)

Data:
list(N=66, y=c(
28, 26, 33, 24, 34, -44, 27, 16, 40, -2,
29, 22, 24, 21, 25, 30, 23, 29, 31, 19,
24, 20, 36, 32, 36, 28, 25, 21, 28, 29,
37, 25, 28, 26, 30, 32, 36, 26, 30, 22,
36, 23, 27, 27, 28, 27, 31, 27, 26, 33,
26, 32, 32, 24, 39, 28, 24, 25, 32, 25,
29, 27, 28, 29, 16, 23))

   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   deviance   408.6   7.969   0.104   393.9   408.3   425.2   1001   10000
   mu   27.49   0.6586   0.00904   26.2   27.49   28.78   1001   10000
   tau   0.04911   0.01186   1.719E-4   0.02955   0.04774   0.07576   1001   10000
   
[example-8_2_1-newcomb3][example-8_2_1-newcomb4][example-8_2_1-newcomb5]