The crux of the method is to compute the probability that an instance belongs to a class C i from a given closed set C, and hence assign the most probable class to a document D, consisting of a vector of n features x 1 ...x n : Bayes' theorem allows us to re-express this as: where P (D) is a normalizing constant independent of C i .
for classification, we only need to estimate P (D|C i ) and P (C i ).