Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach

Many real world applications in medicine, biology, communication networks, web mining, and economics, among others, involve modeling and learning structured stochastic processes that evolve over continuous time. Existing approaches, however, have focused on propo-sitional domains only. Without extensive feature en-gineering, it is difficult—if not impossible—to apply them within relational domains where we may have varying number of objects and relations among them. We therefore develop the first relational representation called Relational Continuous-Time Bayesian Networks (RCTBNs) that can address this challenge. It features a nonparametric learning method that allows for ef-ficiently learning the complex dependencies and their strengths simultaneously from sequence data. Our ex-perimental results demonstrate that RCTBNs can learn as effectively as state-of-the-art approaches for proposi-tional tasks while modeling relational tasks faithfully