Dependent Dirichlet Processes
Abstract
Dependent Dirichlet processes provide a means of modelling a collection of random distributions as related to, but not identical to, each other. A key point is that the realizations of these processes are dependent. This article develops the processes through definition and results. It also describes the basic tools for computations with the processes and suggests a number of applications in which they will be useful. The extension of the Dirichlet process to the dependent Dirichlet process can be paralleled for essentially any other nonparametric process which, for a single random distribution, relies on a countable collection of random variables. A parallel development for two such processes is indicated.
Fields of Study & Subjects
Bayesian Methods and Mixture Models
Statistical Distribution Estimation and Applications
Stochastic processes and statistical mechanics
Publication details
- Journal / source
- Bayesian Analysis
- Publisher
- Institute of Mathematical Statistics
- Volume
- -1
- Issue
- -1
- Pages
- 1-46
- Publication date
- 1 January 2026