# Bayes's Theorem > [!summary] Bayes's Thorem > > $ > P(H\mid x) = \frac{P(x\mid H) P(H)}{P(x)} > $ aka _Bayesian Inference_. - $P(H \mid X)$ - _posterior probability_ - $P(X \mid H)$ - _likelihood_ - $P(H)$ - _prior probability_ - $P(X)$ - evidence ## Naive Bayes - Assume the features are conditionally independent (hence "naïve"), to obtain the likelihood, then apply Bayes' theorem. - Advantages - Easy to implement - Good results in most of the cases - Disadvantages - Assumes there is at least one training object that has any feature value of the test case. Otherwise, the predicted probability will be zero. - Assumes conditional independence.