Scalable MAP Inference in Bayesian Networks Based on a mAP-Reduce Approach
Abstract
Maximum a posteriori (MAP) inference is a particularly complex type of probabilistic inference in Bayesian networks. It consists of finding the most probable configuration of a set of variables of interest given observations on a collection of other variables. In this paper we study scalable solutions to the MAP problem in hybrid Bayesian networks parameterized using conditional linear Gaussian distributions. We propose scalable solutions based on hill climbing and simulated annealing, built on the Apache Flink framework for big data processing. We analyze the scalability of the solution through a series of experiments on large synthetic networks.
Cite
Text
Ramos-López et al. "Scalable MAP Inference in Bayesian Networks Based on a mAP-Reduce Approach." Proceedings of the Eighth International Conference on Probabilistic Graphical Models, 2016.Markdown
[Ramos-López et al. "Scalable MAP Inference in Bayesian Networks Based on a mAP-Reduce Approach." Proceedings of the Eighth International Conference on Probabilistic Graphical Models, 2016.](https://mlanthology.org/pgm/2016/ramoslopez2016pgm-scalable/)BibTeX
@inproceedings{ramoslopez2016pgm-scalable,
title = {{Scalable MAP Inference in Bayesian Networks Based on a mAP-Reduce Approach}},
author = {Ramos-López, Darı́o and Salmerón, Antonio and Rumı́, Rafel and Martı́nez, Ana M. and Nielsen, Thomas D. and Masegosa, Andrés R. and Langseth, Helge and Madsen, Anders L.},
booktitle = {Proceedings of the Eighth International Conference on Probabilistic Graphical Models},
year = {2016},
pages = {415-425},
volume = {52},
url = {https://mlanthology.org/pgm/2016/ramoslopez2016pgm-scalable/}
}