6. Diachronic Interpretation
H -> Hypothesis
D -> Data
P(H) -> Prior Probability
P(H|D) -> Posterior Probability
P(D|H) -> Likelihood
P(D) -> Normalizing Constant
13. How to build a Bayesian Classifier
for prediction
Prepare Data
Features
Extraction
Select
Distribution
Model
Calculate the
Probability for
each attributes
Multiply All
Probabilities
Label with
highest
Probability