Sciweavers

ICML
2005
IEEE

Tempering for Bayesian C&RT

14 years 4 months ago
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.
Nicos Angelopoulos, James Cussens
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2005
Where ICML
Authors Nicos Angelopoulos, James Cussens
Comments (0)