Kernel stick-breaking processes
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- 4 February 2008
- journal article
- research article
- Published by Oxford University Press (OUP) in Biometrika
- Vol. 95 (2) , 307-323
- https://doi.org/10.1093/biomet/asn012
Abstract
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.Keywords
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