Advances in Probabilistic Graphical Models
Author: Peter Lucas, José A. Gámez
Release Date: March 01, 2007
This book brings together important topics of current research in probabilistic graphical modeling, learning from data and probabilistic inference. Coverage includes such topics as the characterization of conditional independence, the learning of graphical models with latent variables, and extensions to the influence diagram formalism as well as important application fields, such as the control of vehicles, bioinformatics and medicine.