Tempering for Bayesian C&RT: ICML 2005

J. Cussens, S. Wrobel

Research output: Other 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
Number of pages8
Publication statusPublished - 2005


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