Approximate Dirichlet Process Computing in Finite Normal Mixtures
- 1 September 2002
- journal article
- Published by Taylor & Francis in Journal of Computational and Graphical Statistics
- Vol. 11 (3) , 508-532
- https://doi.org/10.1198/106186002411
Abstract
A rich nonparametric analysis of the finite normal mixture model is obtained by working with a precise truncation approximation of the Dirichlet process. Model fitting is carried out by a simple Gi...Keywords
This publication has 26 references indexed in Scilit:
- Bayesian Model Selection in Finite Mixtures by Marginal Density DecompositionsJournal of the American Statistical Association, 2001
- Gibbs Sampling Methods for Stick-Breaking PriorsJournal of the American Statistical Association, 2001
- A semiparametric Bayesian model for randomised block designsBiometrika, 1996
- Marginal Likelihood from the Gibbs OutputJournal of the American Statistical Association, 1995
- Bayesian Density Estimation and Inference Using MixturesJournal of the American Statistical Association, 1995
- Optimal Rate of Convergence for Finite Mixture ModelsThe Annals of Statistics, 1995
- Estimating Normal Means with a Dirichlet Process PriorJournal of the American Statistical Association, 1994
- Continuity and weak convergence of ranked and size-biased permutations on the infinite simplexStochastic Processes and their Applications, 1989
- Prior Distributions on Spaces of Probability MeasuresThe Annals of Statistics, 1974
- A Bayesian Analysis of Some Nonparametric ProblemsThe Annals of Statistics, 1973