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 state of each state variable; each temporal variable will then correspond to the time in which a state change occurs. In this chapter we 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.