Log-Logistic Model
model
{
for( i in 1 : N )
{
x[i] ~ dlog.logis(beta, theta)
}
# Prior distributions of the model parameters
beta ~ dunif(0.1, 10.0)
theta~ dunif(0.1, 10.0)
}
The 40 observations are generated from Log-logistic distribution with beta=3.0 and theta = 5.0
The MLEs are beta.mle= 2.97937, theta.mle= 4.82757
Data
( click to open )
Inits for chain 1
Inits for chain 2
( click to open )
Results