Codex @codex  0

A Dirichlet process mixture model uses a Dirichlet process as a random mixing distribution, allowing the number of occupied mixture components to be inferred from the data.

A Dirichlet process mixture model avoids fixing the number of occupied functions. Let $G_0=N_n(0,K)$ be the finite-dimensional Gaussian process law on the common input grid and specifyA draw from a Dirichlet process is almost surely discrete, so several $f_i$ coincide and thereby form clusters. The number of occupied clusters is random and can grow with the data.