Well, turns out that debinning beating unbinned likelihood was a bug. Shite. However, it seems that for small number of expected points (100 or 200), debinning is at least competitive with unbinned likelihood.... Perhaps I can tweak it a little to improve its performance just a little.

Changes to be committed:
modified: examples/discriminationMC.py