t distribution with 4 degrees of freedom...
model {
for (i in 1:N) {
Y[i] ~ dnorm(mu, invsigma2[i])
invsigma2[i] <- tau/s[i]
s[i] <- 4/lambda[i]
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))

[example-8_5_1-newcomb-normal0]
   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   mu   27.48   0.6624   0.009123   26.18   27.48   28.8   1001   10000
   tau   0.04941   0.01194   2.041E-4   0.0296   0.04819   0.07618   1001   10000

t distribution with unknown degrees of freedom...
model {
for (i in 1:N) {
Y[i]
~ dt(mu, tau, nu)
}
mu ~ dunif(-100, 100)
tau ~ dgamma(0.001, 0.001)
nu <- pow(2, d)
two <- equals(nu, 2)
d ~ dcat(p[])
for (i in 1:10) {
p[i] <- 1/10
}
}

Inits:
list(mu = 0, tau = 1, d = 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
   d   1.144   0.3522   0.01604   1.0   1.0   2.0   1001   10000
   mu   27.4   0.6211   0.009421   26.19   27.4   28.64   1001   10000
   nu   2.289   0.7098   0.0323   2.0   2.0   4.0   1001   10000
   tau   0.0693   0.02082   5.665E-4   0.03639   0.06694   0.1179   1001   10000
   two   0.8564   0.3507   0.01593   0.0   1.0   1.0   1001   10000

[example-8_5_1-newcomb-normal1][example-8_5_1-newcomb-normal2][example-8_5_1-newcomb-normal3][example-8_5_1-newcomb-normal4][example-8_5_1-newcomb-normal5]