  We re-visit the role model strategy introduced in an earlier paper, which
allows one to train an estimator for degraded observations by imitating a
reference estimator that has access to superior observations. We show that,
while it is true and surprising that this strategy yields the optimal Bayesian
estimator for the degraded observations, it in fact reduces to a much simpler
form in the non-parametric case, which corresponds to a type of Monte Carlo
integration. We then show an example for which only parametric estimation can
be implemented and discuss further applications for discrete parametric
estimation where the role model strategy does have its uses, although it loses
claim to optimality in this context.
