{"type":"ScholarlyArticle","title":"Handling Overlaps When Lifting Gaussian Bayesian Networks","authors":[{"name":"Mattis Hartwig","slug":"mattis-hartwig","given":"Mattis","family":"Hartwig","affiliation":""},{"name":"Tanya Braun","slug":"tanya-braun","given":"Tanya","family":"Braun","affiliation":""},{"name":"Ralf Möller","slug":"ralf-moller","given":"Ralf","family":"Möller","affiliation":""}],"abstract":"Gaussian Bayesian networks are widely used for modeling the behavior of continuous random variables. Lifting exploits symmetries when dealing with large numbers of isomorphic random variables. It provides a more compact representation for more efficient query answering by encoding the symmetries using logical variables. This paper improves on an existing lifted representation of the joint distribution represented by a Gaussian Bayesian network (lifted joint), allowing overlaps between the logical variables. Handling overlaps without grounding a model is critical for modelling real-world scenarios.\r\nSpecifically, this paper contributes (i) a lifted joint that allows overlaps in logical variables and (ii) a lifted query answering algorithm using the lifted joint. Complexity analyses and experimental results show that - despite overlaps - constructing a lifted joint and answering queries on the lifted joint outperform their grounded counterparts significantly.","doi":"10.24963/ijcai.2021/581","url":"https://ijcai.org/proceedings/2021/581","pdf":"https://ijcai.org/proceedings/2021/0581.pdf","markdown":"https://ijcai.org/proceedings/2021/581.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/581","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Uncertainty in AI","track":"Main Track","number":581,"pages":{"first":4228,"last":4234},"keywords":["Uncertainty in AI: Bayesian Networks","Uncertainty in AI: Exact Probabilistic Inference","Uncertainty in AI: Statistical Relational AI","Uncertainty in AI: Graphical Models"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}