Herded Gibbs Sampling
Machine Learning
2013-03-19 v2 Computation
Machine Learning
Abstract
The Gibbs sampler is one of the most popular algorithms for inference in statistical models. In this paper, we introduce a herding variant of this algorithm, called herded Gibbs, that is entirely deterministic. We prove that herded Gibbs has an convergence rate for models with independent variables and for fully connected probabilistic graphical models. Herded Gibbs is shown to outperform Gibbs in the tasks of image denoising with MRFs and named entity recognition with CRFs. However, the convergence for herded Gibbs for sparsely connected probabilistic graphical models is still an open problem.
Cite
@article{arxiv.1301.4168,
title = {Herded Gibbs Sampling},
author = {Luke Bornn and Yutian Chen and Nando de Freitas and Mareija Eskelin and Jing Fang and Max Welling},
journal= {arXiv preprint arXiv:1301.4168},
year = {2013}
}
Comments
19 pages, including the appendix. Submission for ICLR 2013