Kernel stick-breaking processes
- PMID: 18800173
- PMCID: PMC2538628
- DOI: 10.1093/biomet/asn012
Kernel stick-breaking processes
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.
Figures
References
-
- ALDOUS DJ. Exchangeability and related topics. École d’ Été de Probabilités de Saint-Flour XII. In: Hennequin PL, editor. Springer Lecture Notes Math. Vol. 1117. Berlin: Springer; 1985. pp. 1–198.
-
- BLACKWELL D, MACQUEEN JB. Ferguson distributions via Pólya urn schemes. Ann Statist. 1973;1:353–5.
-
- BARRY D, HARTIGAN JA. Product partition models for change point problems. Ann Statist. 1992;20:260–79.
-
- CARON F, DAVY M, DOUCET A, DUFLOS E, VANHEEGHE P. Bayesian inference for dynamic models with Dirichlet process mixtures. International Conference on Information Fusion; Florence, Italy: INRIA - CCSd - CNRS. 2006. pp. 1–8.
-
- CIFARELLI DM, REGAZINNI E. Nonparametric statistical problems under partial exchangeability: The use of associative means. Ann Inst Mat Finian Univ Torino, II. 1978;12:1–36.
