Query the model: precomputations for efficient inference with Bayesian Networks
Databases
2021-01-29 v4
Abstract
Variable Elimination is a fundamental algorithm for probabilistic inference over Bayesian networks. In this paper, we propose a novel materialization method for Variable Elimination, which can lead to significant efficiency gains when answering inference queries. We evaluate our technique using real-world Bayesian networks. Our results show that a modest amount of materialization can lead to significant improvements in the running time of queries. Furthermore, in comparison with junction tree methods that also rely on materialization, our approach achieves comparable efficiency during inference using significantly lighter materialization.
Cite
@article{arxiv.1904.00079,
title = {Query the model: precomputations for efficient inference with Bayesian Networks},
author = {Cigdem Aslay and Martino Ciaperoni and Aristides Gionis and Michael Mathioudakis},
journal= {arXiv preprint arXiv:1904.00079},
year = {2021}
}