Tempering for Bayesian C&RT

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.

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Nicos Angelopoulos, James Cussens
• Keywords: Exact Computation, Full Bayesian Inference, ICML 2005, Machine Learning, Possible Markov Chain