You have Guest access.
Log In
Dawn E. Holmes and Lakhmi C. Jain
Front matter
1-5
Introduction to Bayesian Networks
7-32
A Polemic for Bayesian Statistics
33-82
A Tutorial on Learning with Bayesian Networks
83-116
The Causal Interpretation of Bayesian Networks
117-130
An Introduction to Bayesian Networks and Their Contemporary Applications
131-167
Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer
169-185
Modeling the Temporal Trend of the Daily Severity of an Outbreak Using Bayesian Networks
187-217
An Information-Geometric Approach to Learning Bayesian Network Topologies from Data
219-249
Causal Graphical Models with Latent Variables: Learning and Inference
251-280
Use of Explanation Trees to Describe the State Space of a Probabilistic-Based Abduction Problem
281-288
Toward a Generalized Bayesian Network
289-317
A Survey of First-Order Probabilistic Models
Back matter
This page requires script.
Frequently asked questions General info on journals and books Send us your feedback Impressum Contact us
© Springer, Part of Springer Science+Business Media Privacy, Disclaimer, Terms & Conditions, and Copyright Info