Bayesian Generalized Kernel Models
Abstract
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.
Cite
Text
Zhang et al. "Bayesian Generalized Kernel Models." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Zhang et al. "Bayesian Generalized Kernel Models." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/zhang2010aistats-bayesian-a/)BibTeX
@inproceedings{zhang2010aistats-bayesian-a,
title = {{Bayesian Generalized Kernel Models}},
author = {Zhang, Zhihua and Dai, Guang and Wang, Donghui and Jordan, Michael I.},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {972-979},
volume = {9},
url = {https://mlanthology.org/aistats/2010/zhang2010aistats-bayesian-a/}
}