  Recently a Bayesian methodology has been introduced, enabling the
construction of sliding window detectors with the constant false alarm rate
property. The approach introduces a Bayesian predictive inference approach,
where under the assumption of no target, a predictive density of the cell under
test, conditioned on the clutter range profile, is produced. The probability of
false alarm can then be produced by integrating this density. As a result of
this, for a given clutter model, the Bayesian constant false alarm rate
detector is produced. This note outlines how this approach can be extended, to
allow the construction of alternative Bayesian decision rules, based upon more
useful measures of the clutter level.
