Tempering for Bayesian C&RT

Nicos Angelopoulos, James Cussens
2005 Proceedings of the 22nd international conference on Machine learning - ICML '05  
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.
doi:10.1145/1102351.1102354 dblp:conf/icml/AngelopoulosC05 fatcat:xsdpidvb4becvhqeoimo3qkje4