Kernel stick-breaking processes.
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.
Dunson
David B
DB
Biostatistics Branch, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, North Carolina 27709, U.S.A. dunson1@niehs.nih.gov.
Park
Ju-Hyun
JH
Lancet. 2001 Jul 14;358(9276):110-4
11463412
Bioinformatics. 2004 May 22;20(8):1222-32
14871871
Biostatistics. 2006 Oct;7(4):551-68
16488893
Biometrics. 2006 Dec;62(4):1044-52
17156278