[pymvpa] BayesConfusionHypothesis clarification

[Hanson, Gavin Keith]

Hi all - I just want to make sure that I’m doing this right.
In the ‘default' implementation of the BayesConfusionHypothesis node, i.e.
	cv = CrossValidation(clf, NFoldPartitioner(), errorfx=None, postproc=ChainNode([Confusion(labels=np.unique(ds.targets)), BayesConfusionHypothesis()]))
it’s set up to use a flat prior, correct?
If that’s the case, then if I have a set of participants and a nice anatomical ROI for each one, could I do a “group” bayesian hypothesis test for the information encoded in that ROI by chaining the posterior probabilities for one into the prior for the next, and the posterior for that into the prior of the next, etc? 
And if yes, then is the prior_Hs argument looking for an array of probabilities, or log probabilities? 
Finally, if I insure that my targets are in the same relative order for each subject - that is, ds.UT is in the same order for everyone, can I safely assume that I don’t need to explicitly pass a list of hypotheses to the ‘hypotheses' argument every time to make sure my array of priors is lining up with the appropriate hypotheses?
Thanks!
- Gavin Hanson