Evidence Estimation for Bayesian Partially Observed MRFs
Abstract
Bayesian estimation in Markov random fields is very hard due to the intractability of the partition function. The introduction of hidden units makes the situation even worse due to the presence of potentially very many modes in the posterior distribution. For the first time we propose a comprehensive procedure to address one of the Bayesian estimation problems, approximating the evidence of partially observed MRFs based on the Laplace approximation. We also introduce a number of approximate MCMC-based methods for comparison but find that the Laplace approximation significantly outperforms these.
Cite
Text
Chen and Welling. "Evidence Estimation for Bayesian Partially Observed MRFs." International Conference on Artificial Intelligence and Statistics, 2013.Markdown
[Chen and Welling. "Evidence Estimation for Bayesian Partially Observed MRFs." International Conference on Artificial Intelligence and Statistics, 2013.](https://mlanthology.org/aistats/2013/chen2013aistats-evidence/)BibTeX
@inproceedings{chen2013aistats-evidence,
title = {{Evidence Estimation for Bayesian Partially Observed MRFs}},
author = {Chen, Yutian and Welling, Max},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2013},
pages = {178-186},
url = {https://mlanthology.org/aistats/2013/chen2013aistats-evidence/}
}