Dependent mixtures of Dirichlet processes
An approach to modeling dependent nonparametric random density functions is presented. This is based on the well known mixture of Dirichlet process model. The idea is to use a technique for constructing dependent random variables, first used for dependent gamma random variables. While the methodology works for an arbitrary number of dependent random densities, with each pair having their own dependent structure, the mathematics and estimation algorithm is focused on two dependent random density functions. Simulations and a real data example are presented.
Year of publication: |
2011
|
---|---|
Authors: | Hatjispyros, Spyridon J. ; Nicoleris, Theodoros ; Walker, Stephen G. |
Published in: |
Computational Statistics & Data Analysis. - Elsevier, ISSN 0167-9473. - Vol. 55.2011, 6, p. 2011-2025
|
Publisher: |
Elsevier |
Keywords: | Bayesian nonparametric inference Bivariate distribution Mixture of Dirichlet process |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
A Bayesian nonparametric study of a dynamic nonlinear model
Hatjispyros, Spyridon J., (2009)
-
A Flaming-Viot Process and Bayesian non Parametric
Nicoleris, Theodoros, (2006)
-
A Fleming-Viot Process and Bayesian Nonparametrics
Walker, Stephen G., (2007)
- More ...