[Spambayes] RE: Central Limit Theorem??!! :)

[Gary Robinson]

You noted that when the training set is smaller smaller values for a (what I
am now calling s ;)  ) work better.

The denominator of f(w) is (s+n). Clearly when there are smaller n's, s has
a bigger impact.

In an ideal Bayesian world, s would represent the strength of our a priori
beliefs and would be independent of n. But  here we are tuning s strictly
for performance, and there are all sorts of subtle interactions that depend
on n because of the generally non-pure approach we are taking.

So it doesn't surprise me that in this application s would be sensitive to
n.

It may be a reason to test on a constant n for now and derive a table for
ideal s values later...

Still seems better than p(w), which  is assuming s is 0! That's just another
decision for a particular constant s... there's no getting getting away from
the issue.

Gary