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State Variable Effects in Graphical Event Models
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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 pointdoi:10.24963/ijcai.2020/587 dblp:conf/ijcai/BremenK20 fatcat:xwaahnxqwvarhhjell37vh7jee