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
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