DWGEE2 give inference for a hypothetical population of complete clusters that is, in general, neither unique nor of scientific interest.
The study of proposition forms can, in fact, be made to include that of inference forms in the following way: let the premises of any given inference form (taken together) be abbreviated by alpha and its conclusion by beta.
Then, different algorithms to implement a given inference system should be specified as strategies to apply the inference rules.
In this way, descriptively accurate historicist observations about when a given inference is rational can be given an epistemically compelling internalist explanation in terms of their levels of confidence.
In this interpretation, the Popperian evidence for a phylogenetic tree hypothesis is a measure of the goodness-of-fit (as defined by any given inference method) of observed character data to that hypothesis.
We show these methods can be seen as actually giving inference for complete clusters and may not also give observed-cluster inference.
Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used.
Earlier studies that have compared gap treatment methods have been confined to comparing their relative performances within a given inference method, particularly MP [ 5, 6].
weighted and doubly weighted generalized estimating equations (GEE) and shared random effects models, can be seen as actually giving inference for complete clusters.
