Discriminative vs Generative Models
There are two approaches to classification, discriminative and generative models.
# Discriminative Models
Discrimintive models aim to classify objects by just knowing the notes/Posterior Probability. The hard problem that needs to be solved is how we can calculate those probabilities, and to solve it, we need to make strong assumptions.
# Generative Models
Generative models aim to calculate the posterior probability by knowing the Class Prior and the conditional density($p(x|\omega)$). Using the principle: $$ p(\omega|x) \propto p(\omega)p(x|\omega) $$ It requires the class prior and the conditional density to be estimated.