Kernel stick-breaking processes
Journal Article (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