Gaussian Naive Bayes
Gaussian Naive Bayes is useful when working with continuous values which probabilities can be modeled using a Gaussian distribution:
Multinomial naive Bayes
A multinomial distribution is useful to model feature vectors where each value represents, for example, the number of occurrences of a term or its relative frequency. If the feature vectors have n elements and each of them can assume k different values with probability pk, then:
Bernoulli naive Bayes
If X is random variable Bernoulli-distributed, it can assume only two values (for simplicity, let’s call them 0 and 1) and their probability is: