After we created a Naive Bayes classifier object nb (say, with multivariate multinomial (mvmn) distribution), we can call posterior function on testing data using nb object. This function has 3 output parameters:
[post,cpre,logp] = posterior(nb,test)
I understand how post is computed and the meaning of that, also cpre is the predicted class, based on the maximum over posterior probabilities for each class.
The question is about logp. It is clear how it is computed (logarithm of the PDF of each pattern in test), but I don't understand the meaning of this measure and how it can be used in the context of Naive Bayes procedure. Any light on this is very much appreciated.
Thanks.