Anytime Marginal MAP Inference
Abstract
This paper presents a new anytime algorithm for the marginal MAP problem in graphical models of bounded treewidth. We show asymptotic convergence and theoretical error bounds for any fixed step. Experiments show that it compares well to a state-of-the-art systematic search algorithm.
Cite
Text
Mauá and de Campos. "Anytime Marginal MAP Inference." International Conference on Machine Learning, 2012.Markdown
[Mauá and de Campos. "Anytime Marginal MAP Inference." International Conference on Machine Learning, 2012.](https://mlanthology.org/icml/2012/maua2012icml-anytime/)BibTeX
@inproceedings{maua2012icml-anytime,
title = {{Anytime Marginal MAP Inference}},
author = {Mauá, Denis Deratani and de Campos, Cassio Polpo},
booktitle = {International Conference on Machine Learning},
year = {2012},
url = {https://mlanthology.org/icml/2012/maua2012icml-anytime/}
}