Dynamic and Temporal Bayesian Networks

Abstract

Dynamic Bayesian network models extend BNs to represent the temporal evolution of a certain process. There are two basic types of Bayesian network models for dynamic processes: state based and event based. Dynamic Bayesian networks are state-based models that represent the state of each variable at discrete time intervals. Event-based models represent the changes in the state of each state variable; each temporal variable will then correspond to the time in which a state change occurs. In this chapter, we will review dynamic Bayesian networks and event networks, including representation, inference, and learning. The chapter includes two application examples: dynamic Bayesian networks for gesture recognition and temporal nodes Bayesian networks for HIV mutational pathways prediction.