[Limdep Nlogit List] Difference between hierarchical Bayes and random parameter models?

[Richard Turner]

Greetings,

>From my limited understanding, the difference is mainly that hierarchical
Bayes (HB) incorporates parameter distribution priors that will "constrain"
the individual parameters to one side of the distribution. Conversely,
random parameter/effects models (RPM) simply pick the individual parameter
randomly from the distribution, but one can constrain the parameter to a
location of the distribution by incorporating other covariates such as
socio-demographic variables.

Questions:

Is this the main difference? If so, I see that in theory HB would predict
better in small samples. What I am not sure about is how controlling for
individual covariates (to constrain the parameter to a location of the
distribution) in a RPM would produce worse (or better) predictions than a
HB model.
than the other.
Does anyone know of any good tutorials in R (or any other software) that
show how to incorporate parameter distribution priors in HB? Unfortunately,
I've read too many "Hierarchical Bayes" tutorials that leave this out.
Any other suggested material to read is greatly appreciated.

Best regards,

R