{"type":"ScholarlyArticle","title":"State Variable Effects in Graphical Event Models","authors":[{"name":"Debarun Bhattacharjya","slug":"debarun-bhattacharjya","given":"Debarun","family":"Bhattacharjya","affiliation":"Research AI , IBM T. J. Watson Research Center"},{"name":"Dharmashankar Subramanian","slug":"dharmashankar-subramanian","given":"Dharmashankar","family":"Subramanian","affiliation":"Research AI , IBM T. J. Watson Research Center"},{"name":"Tian Gao","slug":"tian-gao","given":"Tian","family":"Gao","affiliation":"Research AI , IBM T. J. Watson Research Center"}],"abstract":"Many real-world domains involve co-evolving relationships between events, such as meals and exercise, and time-varying random variables, such as a patient's blood glucose levels. In this paper, we propose a general framework for modeling joint temporal dynamics involving continuous time transitions of discrete state variables and irregular arrivals of events over the timeline. We show how conditional Markov processes (as represented by continuous time Bayesian networks) and multivariate point processes (as represented by graphical event models) are among various processes that are covered by the framework. We introduce and compare two simple and interpretable yet practical joint models within the framework with relevant baselines on simulated and real-world datasets, using a graph search algorithm for learning. The experiments highlight the importance of jointly modeling event arrivals and state variable transitions to better fit joint temporal datasets, and the framework opens up possibilities for models involving even more complex dynamics whenever suitable.","doi":"10.24963/ijcai.2020/592","url":"https://ijcai.org/proceedings/2020/592","pdf":"https://ijcai.org/proceedings/2020/0592.pdf","markdown":"https://ijcai.org/proceedings/2020/592.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/592","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Uncertainty in AI","track":"Main track","number":592,"pages":{"first":4291,"last":4297},"keywords":["Uncertainty in AI: Uncertainty Representations","Machine Learning: Learning Graphical Models","Data Mining: Mining Spatial, Temporal Data","Uncertainty in AI: Graphical Models"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24932/file_24932.m3u8","length_sec":281,"provider":"hls"}],"references":[{"position":1,"raw":"[Bhattacharjya et al., 2018] D. 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