Published In Towards Inference and Learning in Dynamic Bayesian Networks using Generalized Evidence

This report introduces a novel approach to performing inference and learning inDynamic Bayesian Networks (DBN). The traditional approach to inference and learning in DBNs involves condition-ing on one or more finite-length observation sequences. In this report, we consider conditioning on what we will call generalized evidence, which consists of a possibly infinite set of behaviors com-pactly encoded in the form of a formula, φ, in temporal logic. We then introduce exact algorithms for solving inference problems (i.e., computing P (X|φ)) and learning problems (i.e., computing P (Θ|φ)) using techniques from the field of Model Checking. The advantage of our approach is that it enables scientists to pose and solve inference and learning problems that cannot be ex-pressed using traditional approaches. The contributions of this report include: (1) the introduction of the inference and learning problems over generalized evidence, (2) exact algorithms for solving these problems for a restricted class of DBNs, and (3) a series of case studies demonstrating th