Kernel stick-breaking processes

Published

Journal Article

We propose a class of kernel stick-breaking processes for uncountable collections of dependent random probability measures. The process is constructed by first introducing an infinite sequence of random locations. Independent random probability measures and beta-distributed random weights are assigned to each location. Predictor-dependent random probability measures are then constructed by mixing over the locations, with stick-breaking probabilities expressed as a kernel multiplied by the beta weights. Some theoretical properties of the process are described, including a covariate-dependent prediction rule. A retrospective Markov chain Monte Carlo algorithm is developed for posterior computation, and the methods are illustrated using a simulated example and an epidemiological application.© US Government/Department of Health and Human Services 2008.

Full Text

Duke Authors

Cited Authors

  • Dunson, DB; Park, JH

Published Date

  • June 1, 2008

Published In

Volume / Issue

  • 95 / 2

Start / End Page

  • 307 - 323

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asn012

Citation Source

  • Scopus