Hello,
I have a number of field observations that follow a truncated normal
distribution (lower bound= 80, upper bound=100).
I need to know the mean of the underlying normal distribution. But this mean,
based on prior background, should be somewhere between the values 90 and
120, not more and not less.
I'd like to know if this model is plausible:
model
{
# likelihood
for (i in 1:N) {
y[i] ~ djl.dnorm.trunc(mu, tausq, 80, 100)
}
# priors
mu ~ dunif(90, 120)
tausq ~ dgamma( 0.0001, 0.0001)
}
Thanks in advance
David
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