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: [email protected]
Skype: mtettamanti
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