State Variable Effects in Graphical Event Models

Debarun Bhattacharjya, Dharmashankar Subramanian, Tian Gao
2020 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 point
more » ... ocesses (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.2020/587 dblp:conf/ijcai/BremenK20 fatcat:xwaahnxqwvarhhjell37vh7jee