Innovations in Bayesian Networks

Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume the editors have brought together contributions from some of the most prestigious researchers in this field.

Presents the innovative paradigms related to the theory and practical applications of Bayesian Networks

Inhalt
to Bayesian Networks.- A Polemic for Bayesian Statistics.- A Tutorial on Learning with Bayesian Networks.- The Causal Interpretation of Bayesian Networks.- An Introduction to Bayesian Networks and Their Contemporary Applications.- Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer.- Modeling the Temporal Trend of the Daily Severity of an Outbreak Using Bayesian Networks.- An Information-Geometric Approach to Learning Bayesian Network Topologies from Data.- Causal Graphical Models with Latent Variables: Learning and Inference.- Use of Explanation Trees to Describe the State Space of a Probabilistic-Based Abduction Problem.- Toward a Generalized Bayesian Network.- A Survey of First-Order Probabilistic Models.