Tempering for Bayesian C&RT

J. Cussens, S. Wrobel (Editor), N. Angelopoulos, L. De Raedt (Editor)

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


This paper concerns the experimental assessment of tempering as a technique for improving Bayesian inference for C&RT models. Full Bayesian inference requires the computation of a posterior over all possible trees. Since exact computation is not possible Markov chain Monte Carlo (MCMC) methods are used to produce an approximation. C&RT posteriors have many local modes: tempering aims to prevent the Markov chain getting stuck in these modes. Our results show that a clear improvement is achieved using tempering.
Original languageEnglish
Title of host publicationProceedings of the 22nd International Conference on Machine Learning
Number of pages7
Publication statusPublished - 2005
EventICML 2005 - Bonn, Germany
Duration: 7 Aug 200511 Aug 2005

Publication series

NameACM International Conference Proceeding Series


ConferenceICML 2005
CityBonn, Germany

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