Dirichletprosessipohjaiset
Dirichletprosessipohjaiset refers to statistical models and machine learning techniques that are built on the Dirichlet process, a stochastic process used in Bayesian nonparametric statistics. The Dirichlet process provides a flexible prior over probability distributions, allowing the number of underlying components in a mixture model to grow with the data rather than being fixed a priori. This property makes Dirichletprocess-based methods particularly suited for problems where the complexity of the underlying structure is unknown, such as clustering, density estimation, and topic modelling.
A Dirichlet process is defined by a concentration parameter and a base distribution. The concentration parameter
Dirichletprocess-based models include the Dirichlet process mixture model (DPMM) and hierarchical variants such as the hierarchical
Common inference techniques for Dirichletprocess-based models are Markov chain Monte Carlo (MCMC) algorithms, including Gibbs sampling,