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))
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