• Assumes the features are independent and contributes to the predict (Class conditional assumption)
• Based on bayes theorem

H - Hypothesis E - Evidence P(H) - Prior Probability P(H|E) - Posterior Probability P(E|H) - Likelihood Ratio

Probability of occurrence of hypothesis H given E is,

P(H|E) = P(E|H) * P(H) / P(E)

P(A|B) = P(A inter B) / P(B)

P(B|A) = P(B inter A) / P(A)

Types #

• Guassian - Normally distributed data
• Multinomial -
• Bernoulli - Feature vectors are binary