Dirichletprosessi
Dirichlet process (DP) is a stochastic process used in Bayesian nonparametric statistics, primarily as a prior distribution over distributions. It provides a flexible framework for modeling data where the number of underlying components or clusters is unknown and potentially infinite. The Dirichlet process is parameterized by a base distribution, typically denoted as G₀, which represents the expected distribution, and a concentration parameter, α, which influences the clustering behavior.
Formally, a Dirichlet process, denoted as DP(α, G₀), is a distribution over probability measures. It can be
The DP's primary use is in mixture modeling, such as Dirichlet Process Mixture Models (DPMMs), where it
Theoretical properties of the Dirichlet process include conjugacy, which facilitates analytical posterior updates, and its role