Uncertain and negative evidence in continuous time Bayesian networks

• Created a taxonomy of discrete-state, continuous-time evidence types.
• Showed generalization and combination relationships between evidence types.
• Demonstrated the effects of evidence types on a real-world network.
• Extended exact and approximate inference for CTBNs to handle new evidence types.
• Demonstrated convergence and scaling of CTBN approximate inference algorithm.

The continuous time Bayesian network (CTBN) enables reasoning about complex systems by representing the system as a factored, finite-state, continuous-time Markov process. Inference over the model incorporates evidence, given as state observations through time. The time dimension introduces several new types of evidence that are not found with static models. In this work, we present a comprehensive look at the types of evidence in CTBNs. Moreover, we define and extend inference to reason under uncertainty in the presence of uncertain evidence, as well as negative evidence, concepts extended to static models but not yet introduced into the CTBN model.