  We describe a four-level hierarchy mapping both all discrete estimation
problems and all estimators on these problems, such that the hierarchy
describes each estimator's consistency guarantees on each problem class. We
show that no estimator is consistent for all estimation problems, but that some
estimators, such as Maximum A Posteriori, are consistent for the widest
possible class of discrete estimation problems. For Maximum Likelihood and
Approximate Maximum Likelihood estimators we show that they do not provide
consistency on as wide a class, but define a sub-class of problems
characterised by their consistency. Lastly, we show that some popular
estimators, specifically Strict Minimum Message Length, do not provide
consistency guarantees even within the sub-class.
