Naïve Bayesian classifiers  are simple probabilistic classifiers with their foundation on application of Bayes’ theorem with the assumption of strong (naïve) independence among the features. The following equation  states Bayes’ theorem in mathematical terms:
A and B are events
P(A) and P(B) are the prior probabilities of A and B without regard to each other
P(A|B), also called posterior probability, is the probability of observing event A given that B is true
P(B|A), also called likelihood, is the probability of observing event B given that A is true
Suppose that vector X = (x1, x2, … xn) is an instance (with n independent features) to be classified ...