A Computational Approach for Full Nonparametric Bayesian Inference Under Dirichlet Process Mixture Models
- 1 June 2002
- journal article
- Published by Taylor & Francis in Journal of Computational and Graphical Statistics
- Vol. 11 (2) , 289-305
- https://doi.org/10.1198/106186002760180518
Abstract
Widely used parametric generalized linear models are, unfortunately, a somewhat limited class of specifications. Nonparametric aspects are often introduced to enrich this class, resulting in semipa...Keywords
This publication has 26 references indexed in Scilit:
- The direct use of likelihood for significance testingStatistics and Computing, 1997
- A semiparametric Bayesian model for randomised block designsBiometrika, 1996
- Bayesian Density Estimation and Inference Using MixturesJournal of the American Statistical Association, 1995
- Bayesian Nonparametric Estimation for Incomplete Data Via Successive Substitution SamplingThe Annals of Statistics, 1994
- Estimating Normal Means with a Dirichlet Process PriorJournal of the American Statistical Association, 1994
- Bayesian Nonparametric Estimation of the Median; Part II: Asymptotic Properties of the EstimatesThe Annals of Statistics, 1985
- Bayesian Nonparametric Estimation of the Median; Part I: Computation of the EstimatesThe Annals of Statistics, 1985
- Dirichlet invariant processes and applications to nonparametric estimation of symmetric distribution functionsStochastic Processes and their Applications, 1979
- Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric ProblemsThe Annals of Statistics, 1974
- A Bayesian Analysis of Some Nonparametric ProblemsThe Annals of Statistics, 1973