Scalable MAP inference in Bayesian networks based on a Map-Reduce approach


Darı́o Ramos-López, Antonio Salmerón, Rafel Rumı́, Ana M. Martı́nez, Thomas D. Nielsen, Andrés R. Masegosa, Helge Langseth, Anders L. Madsen ;
Proceedings of the Eighth International Conference on Probabilistic Graphical Models, PMLR 52:415-425, 2016.


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.

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