Lifted Query Answering in Gaussian Bayesian Networks

Mattis Hartwig, Ralf Möller
Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:233-244, 2020.

Abstract

Gaussian Bayesian networks are widely used for modeling behaviors of continuous random variables. Lifting exploits symmetries when dealing with large numbers of isomorphic random variables to support more compact representations and more efficient query answering. This paper presents a lifted construction and representation of a joint distribution derived from a Gaussian Bayesian network and a lifted query answering algorithm on the lifted joint distribution. To lift the query answering, needed algebraic operations that work fully in the lifted space are developed. A theoretical complexity analysis and experimental results show that both the lifted joint construction and the lifted query answering significantly outperform their grounded counterparts.

Cite this Paper


BibTeX
@InProceedings{pmlr-v138-hartwig20a, title = {Lifted Query Answering in Gaussian Bayesian Networks}, author = {Hartwig, Mattis and M\"oller, Ralf}, booktitle = {Proceedings of the 10th International Conference on Probabilistic Graphical Models}, pages = {233--244}, year = {2020}, editor = {Manfred Jaeger and Thomas Dyhre Nielsen}, volume = {138}, series = {Proceedings of Machine Learning Research}, month = {23--25 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v138/hartwig20a/hartwig20a.pdf}, url = { http://proceedings.mlr.press/v138/hartwig20a.html }, abstract = {Gaussian Bayesian networks are widely used for modeling behaviors of continuous random variables. Lifting exploits symmetries when dealing with large numbers of isomorphic random variables to support more compact representations and more efficient query answering. This paper presents a lifted construction and representation of a joint distribution derived from a Gaussian Bayesian network and a lifted query answering algorithm on the lifted joint distribution. To lift the query answering, needed algebraic operations that work fully in the lifted space are developed. A theoretical complexity analysis and experimental results show that both the lifted joint construction and the lifted query answering significantly outperform their grounded counterparts. } }
Endnote
%0 Conference Paper %T Lifted Query Answering in Gaussian Bayesian Networks %A Mattis Hartwig %A Ralf Möller %B Proceedings of the 10th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2020 %E Manfred Jaeger %E Thomas Dyhre Nielsen %F pmlr-v138-hartwig20a %I PMLR %P 233--244 %U http://proceedings.mlr.press/v138/hartwig20a.html %V 138 %X Gaussian Bayesian networks are widely used for modeling behaviors of continuous random variables. Lifting exploits symmetries when dealing with large numbers of isomorphic random variables to support more compact representations and more efficient query answering. This paper presents a lifted construction and representation of a joint distribution derived from a Gaussian Bayesian network and a lifted query answering algorithm on the lifted joint distribution. To lift the query answering, needed algebraic operations that work fully in the lifted space are developed. A theoretical complexity analysis and experimental results show that both the lifted joint construction and the lifted query answering significantly outperform their grounded counterparts.
APA
Hartwig, M. & Möller, R.. (2020). Lifted Query Answering in Gaussian Bayesian Networks. Proceedings of the 10th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 138:233-244 Available from http://proceedings.mlr.press/v138/hartwig20a.html .

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