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, accounting for the "missing" upper and lower tails of the 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