Dirichlet process

Summary

A Dirichlet process is a random probability measure whose values on every finite partition of the space follow a Dirichlet distribution. It depends on a concentration parameter $\alpha > 0$ and a base probability measure $H$. It is the basic prior distribution on probability measures in nonparametric Bayesian inference. Its samples are discrete measures almost surely, and the posterior after observing data is again a Dirichlet process.