Bayesian Generalized Kernel Models

10 years 5 months ago
Bayesian Generalized Kernel Models
We propose a fully Bayesian approach for generalized kernel models (GKMs), which are extensions of generalized linear models in the feature space induced by a reproducing kernel. We place a mixture of a point-mass distribution and Silverman's g-prior on the regression vector of GKMs. This mixture prior allows a fraction of the regression vector to be zero. Thus, it serves for sparse modeling and Bayesian computation. For inference, we exploit data augmentation methodology to develop a Markov chain Monte Carlo (MCMC) algorithm in which the reversible jump method is used for model selection and a Bayesian model averaging method is used for posterior prediction.
Zhihua Zhang, Guang Dai, Donghui Wang, Michael I.
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JMLR
Authors Zhihua Zhang, Guang Dai, Donghui Wang, Michael I. Jordan
Comments (0)