Exploiting informative priors for Bayesian classification and regression trees

N. Angelopoulos, J. Cussens, L. Pack Kaelbling (Editor), A. Saffiotti (Editor)

Research output: Chapter in Book/Report/Conference proceedingConference contribution


A general method for defining informative priors on statistical models is presented and applied specifically to the space of classification and regression trees. A Bayesian approach to learning such models from data is taken, with the Metropolis- Hastings algorithm being used to approximately sample from the posterior. By only using proposal distributions closely tied to the prior, acceptance probabilities are easily computable via marginal likelihood ratios, whatever the prior used. Our approach is empirically tested by varying (i) the data, (ii) the prior and (iii) the proposal distribution. A comparison with related work is given.
Original languageEnglish
Title of host publicationProceedings of the Nineteenth International Joint Conference on Artificial Intelligence
PublisherProfessional Book Center
Number of pages5
ISBN (Print)0938075934
Publication statusPublished - 2005
EventIJCAI-05 - Edinburgh, Scotland
Duration: 5 Jul 2005 → …


CityEdinburgh, Scotland
Period5/07/05 → …

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