Mathematics
Bayesian Nonparametrics: Dirichlet Process and Its Applications
Quick fact
The Dirichlet process is a prior over probability distributions itself, meaning it can be used to model infinite mixtures where the number of components is not fixed in advance. This flexibility is why Bayesian nonparametrics underpin modern clustering and topic models.
Why this is interesting
Most models force you to decide how many clusters or topics exist before seeing any data. But what if the data itself could tell you the right number—and keep revealing more as it grows?