[pymvpa] BayesConfusionHypothesis

[marco tettamanti]

Dear all,
first of all I take my first chance to thank the authors for making such a great 
software as pymvpa available!

I have some (beginner) questions regarding the BayesConfusionHypothesis 
algorithm for for multiclass pattern discrimination.

If I understand it correctly, what the algorithm does is to compare all possible 
partitions of classes and it then reports the most likely partitioning 
hypothesis to explain the confusion matrix (i.e. highest log likelihood among 
those of all possible hypotheses, as stored in the .sample attribute).

Apart from being happy to see confimed my hypothesis of all classes being 
discriminable from each other, is there any way to obtain or calculate some 
measures of how likely it is that the most likely hypothesis is truly 
strongly/weakly superior than some or all of the alternative hypotheses?
For instance, Olivetti et al (PRNI 2012) state that a BF>1 is sufficient to 
support H1 over H0 and report Bayes Factor and binomial tests in tables.

I assume I should know the answer, so forgive me for my poor statistics.

On a related matter: I see form the BayesConfusionHypothesis documentation, that 
there should be parameters to define a hypothesis space (space=) or some 
specific hypotheses (hypotheses=).
Could anybody please provide some examples on how to fill in these parameters?

Thank you and all the best,
Marco

-- 
Marco Tettamanti, Ph.D.
Nuclear Medicine Department & Division of Neuroscience
San Raffaele Scientific Institute
Via Olgettina 58
I-20132 Milano, Italy
Phone ++39-02-26434888
Fax ++39-02-26434892
Email: tettamanti.marco at hsr.it
Skype: mtettamanti
--------------------------------------------------------------------------
LA TUA CURA E' SCRITTA NEL TUO DNA. AL SAN RAFFAELE LA STIAMO REALIZZANDO.
AIUTA LA RICERCA, DAI IL TUO 5XMILLE - CF: 07636600962
info:www.5xmille at hsr.it - www.5xmille.org

Disclaimer added by CodeTwo Exchange Rules 2007	
http://www.codetwo.com