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Tempering for Bayesian C&RT

Angelopoulos, N. and Cussens, J. (2005) Tempering for Bayesian C&RT. In: De Raedt, L. and Wrobel, S., (eds.) Proceedings of the 22nd International Conference on Machine Learning. ICML 2005, Aug 7 - 11, 2005, Bonn, Germany. ACM International Conference Proceeding Series (19). , pp. 17-24. ISBN 1-59593-180-5

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Abstract

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.

Item Type: Proceedings Paper
Academic Units: The University of York > Computer Science (York)
Depositing User: York RAE Import
Date Deposited: 07 Apr 2009 09:37
Last Modified: 07 Apr 2009 09:37
Published Version: http://dx.doi.org/10.1145/1102351.1102354
Status: Published
Identification Number: 10.1145/1102351.1102354
URI: http://eprints.whiterose.ac.uk/id/eprint/7325

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