Bayesian density estimation and inference using mixtures

Published

Journal Article

We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dirichlet processes. These models provide natural settings for density estimation and are exemplified by special cases where data are modeled as a sample from mixtures of normal distributions. Efficient simulation methods are used to approximate various prior, posterior, and predictive distributions. This allows for direct inference on a variety of practical issues, including problems of local versus global smoothing, uncertainty about density estimates, assessment of modality, and the inference on the numbers of components. Also, convergence results are established for a general class of normal mixture models. © 1995 Taylor & Francis Group, LLC.

Full Text

Duke Authors

Cited Authors

  • Escobar, MD; West, M

Published Date

  • January 1, 1995

Published In

Volume / Issue

  • 90 / 430

Start / End Page

  • 577 - 588

Electronic International Standard Serial Number (EISSN)

  • 1537-274X

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1080/01621459.1995.10476550

Citation Source

  • Scopus