Graphical Models for Probabilistic and Causal Reasoning.

INTRODUCTIONThis chapter surveys the development of graphical models known as Bayesian networks,summarizes their semantical basis and assesses their properties and applications to reasoningand planning.Bayesian networks are directed acyclic graphs (DAGs) in which the nodes representvariables of interest (e.g., the temperature of a device, the gender of a patient, a feature ofan object, the occurrence of an event) and the links represent causal influences among thevariables. The strength ...