Event-Driven Continuous Time Bayesian Networks


  • Debarun Bhattacharjya IBM T. J. Watson Research Center
  • Karthikeyan Shanmugam IBM T. J. Watson Research Center
  • Tian Gao IBM T. J. Watson Research Center
  • Nicholas Mattei Tulane University
  • Kush Varshney IBM T. J. Watson Research Center
  • Dharmashankar Subramanian IBM T. J. Watson Research Center




We introduce a novel event-driven continuous time Bayesian network (ECTBN) representation to model situations where a system's state variables could be influenced by occurrences of events of various types. In this way, the model parameters and graphical structure capture not only potential “causal” dynamics of system evolution but also the influence of event occurrences that may be interventions. We propose a greedy search procedure for structure learning based on the BIC score for a special class of ECTBNs, showing that it is asymptotically consistent and also effective for limited data. We demonstrate the power of the representation by applying it to model paths out of poverty for clients of CityLink Center, an integrated social service provider in Cincinnati, USA. Here the ECTBN formulation captures the effect of classes/counseling sessions on an individual's life outcome areas such as education, transportation, employment and financial education.




How to Cite

Bhattacharjya, D., Shanmugam, K., Gao, T., Mattei, N., Varshney, K., & Subramanian, D. (2020). Event-Driven Continuous Time Bayesian Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3259-3266. https://doi.org/10.1609/aaai.v34i04.5725



AAAI Technical Track: Machine Learning