Belief Networks

Introduction

• Bayesian Networks Without Tears, by Eugene Charniak, AI Magazine 12(4):50-63, Winter 1991. (PDF)
  • Very basic intro to Bayesian networks for beginners.
• A Tutorial on Learning With Bayesian Networks by David Heckerman
  • A standard (recommended) intro to Bayesian networks
• A Brief Introduction to Graphical Models and Bayesian Networks by Kevin Murphy; (includes pointers to pdf version)
  • Nice intro to Bayesian networks, with ties to his code.
• Belief Networks and Decision Graphs by F. Jensen, Springer 2001.
  • Perhaps the best introductory book on belief nets.
  • the first two chapters
• Judea Pearl
  • nice work on causality (after his seminal Bayes network work)
• Michael Jordan
  • Some very pretty research, including a nice tutorial showing a wide variety of graphical models in the "plate" notation.
• Daphne Koller
  • Probabilistic Relational Networks and much more

General pointers

• The CMU AI repository is a nice collection, including sample code
• Eugene Santos' Bayesian Networks information repository
• reading list from Guang Chen

Software

• Beyond the CMU repository, there are many pointer lists of software, but if you have Matlab, you should just use Kevin Knight's software. If not, you might try the Microsoft Bayesian Network Editor or Hugin's commercial code.

Historical

• Pearl, Judea, "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference", Morgan Kaufmann, San Mateo, California, 1988.
  • Theoretical framework for Bayesian networks - the seminal work, but not the most fun to read first
• Neapolitan, Richard E., "Probabilistic Reasoning in Expert Systems: Theory and Algorithms", John Wiley and Sons, 1990.
  • Practical guide to implementation.

Collections of papers

• Oliver, Robert M., and Smith, James Q., editors, "Influence Diagrams, Belief Nets and Decision Analysis", John Wiley and Sons, 1990.
• Shafer, Glenn, and Pearl, Judea, "Readings in Uncertain Reasoning", Morgan Kaufmann, San Mateo, California, 1990. 768 pages, ISBN 1-55860-125-2 paper ($49.95).

For medical diagnostics

• M. Shwe, B. Middleton, D. Heckerman, M. Henrion, E. Horvitz, H. Lehmann, & G. Cooper. Probabilistic Diagnosis Using a Reformulation of the INTERNIST-1/QMR Knowledge Base I. The Probabilistic Model and Inference Algorithms. Methods of Information in Medicine, 30:241-255, 1991.
• B. Middleton, M. Shwe, D. Heckerman, M. Henrion, E. Horvitz, H. Lehmann, & G. Cooper. Probabilistic Diagnosis Using a Reformulation of the INTERNIST-1/QMR Knowledge Base II. Evaluation of Diagnostic Performance. Methods of Information in Medicine, 30:256-267, 1991.

Learning Dynamic Bayesian Networks

• Z. Ghahramani, In C.L. Giles and M. Gori (eds.), Adaptive Processing of Sequences and Data Structures . Lecture Notes in Artificial Intelligence, 168-197. Berlin: Springer-Verlag. ftp.cs.toronto.edu/pub/zoubin/vietri.ps.gz