https://iq.opengenus.org/bernoulli-naive-bayes/

Bernoulli Naive Bayes Conditional Probability and Reference

https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html#sklearn.naive_bayes.BernoulliNB

Bernoulli Naive Bayes Scikit Reference

In the Bernoulli model, features are independent booleans/binary variables describing inputs. This model is popular for document classification tasks such as word presence, where binary term occurrence features are used rather than term frequencies.

University of Michigan - Ann Arbor

The primary types of Naive Bayes classifiers are Bernoulli Naive Bayes, Multinomial Naive Bayes, and Gaussian Naive Bayes.

Types of Naive Bayes Classifiers

Bernoulli Naive Bayes

 When dealing with continuous data, a typical assumption is that the continuous/real-valued features associated with each class are distributed according to a normal (or Gaussian) distribution. Statistics computed by segmenting the data by the class, and then computing the mean and variance of the continuous attributes in each class.

Gaussian Naive Bayes

With a multinomial model, samples represent the frequencies where certain events have been generated by a multinomial. Typically used for document classification, where there are discrete features like word counts.

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