Disjunctive interaction in continuous time Bayesian networks

A continuous time Bayesian network is a probabilistic graphical model capable of describing discrete state systems that evolve in continuous time. Unfortunately, the number of parameters required for each node in the graph is exponential in the number of parents of the node, which can be prohibitively large for many real-world systems. To mitigate this problem, disjunctive interaction is proposed as a method for reducing the number of required parameters from exponential to linear. In this work, the relation between disjunctive interaction and standard parameterization techniques is explored both theoretically and experimentally. Experimental results demonstrate that inference over models with disjunctive interaction exhibits greater scalability with no degradation in accuracy.