Reading list for Bayes Nets

Bayesian networks textbooks

1. Pearl, J., Probabilistic Reasoning in Intelligent Systems. 1988, San Francisco, California: Morgan Kaufmann.

2. Neapolitan, R.E., Probabilistic reasoning in expert systems: theory and algorithms. 1990, New York: Wiley.

3. Neapolitan, R.E., Learning Bayesian Networks. 2004: Prentice Hall.

4. Jensen, F.V., Bayesian networks and decision graphs. 2001, New York: Springer.

5. Jensen, F.V., An introduction to Bayesian networks. 1996, London: UCL Press.

superseded by his 2001 book

6. Castillo, E., J.M. Gutiérrez, and A.S. Hadi., Expert systems and probabilistic network models. 1997, New York: Springer.

7. Korb, K.B. and A.E. Nicholson, Bayesian artificial intelligence. 2003: Chapman & Hall/CRC.

8. Jordan, M.I., ed. Learning in graphical models. 1998, Kluwer Academic Publishers: Boston.

9. Jordan, M., An Introduction to Probabilistic Graphical Models (to appear).

10. Koller, D. and N. friedman, Bayesian networks and Beyond (to appear).

11. Russell, S.J. and P. Norvig, Artificial intelligence: a modern approach. 2nd ed. 2003: Prentice Hall. (Chapter 13-14)

Causality textbooks

12. Pearl, J., Causality: models, reasoning, and inference. 2000, New York: Cambridge University Press.

13. Spirtes, P., C. Glymour, and R. Scheines, Causation, Prediction, and Search (Second Edition). 2000, Cambridge, MA, USA: MIT Press.

14. Glymour, C. and G.F. Cooper, eds. Computation, Causation, and Discovery. 1999, MIT Press: Cambridge, MA, USA.

Readings in Bayesian networks

15. Charniak, E., Bayesian networks without tears: making Bayesian networks more accessible to the probabilistically unsophisticated. AI Magazine, 1991. 12(4): p. 50-63.

16. Heckerman, D., A Tutorial on Learning with Bayesian Networks, in Learning in Graphical Models, M. Jordan, Editor. 1999, MIT Press: Cambridge, MA. p. 301-354.

17. Murphy, K.P. How to use the Bayes Net Toolbox. [cited October 06, 2005]; Available from: http://www.cs.ubc.ca/~murphyk/Software/BNT/usage.html.

You can download the software and play with it. This manual is a good introduction to the concepts in Bayesian networks with codes and examples.

18. Cooper, G.F. and E. Herskovits, A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 1992. 9: p. 309-347.

K2 method

19. Heckerman, D., D. Geiger, and D.M. Chickering, Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 1995. 20: p. 197-243.

BDe criterion

20. Pearl, J. and T. Verma. A theory of inferred causation. in Principles of Knowledge Representation and Reasoning: Proceeding of the Second International Conference. 1991. San Mateo, CA: Morgan Kaufmann.
IC algorithm

21. Buntine, W., Operations for Learning with Graphical Models. Journal of Artificial Intelligence Research, 1994. 2: p. 159-225.

Dynamic Bayesian networks

22. Dean, T. and K. Kanazawa, A model for reasoning about persistence and causation. Computational Intelligence, 1989. 5(3): p. 142-150.

First paper for Dynamic Bayesian networks

Data mining & Machine learning

23. Fayyad, U.M., G. Piatetsky-Shapiro, and P. Smyth, From data mining to knowledge discovery: An overview, in Advances in knowledge discovery and data mining, U.M. Fayyad, et al., Editors. 1996, AAAI Press: Menlo Park, CA, USA. p. 1-30.

A classic paper for the whole picture of knowledge discovery in database

24. Fayyad, U.M., et al., eds. Advances in knowledge discovery and data mining. 1996, AAAI Press: Menlo Park, Calif.

25. Liu, H., et al., Discretization: An Enabling Technique. Data Mining and Knowledge Discovery, 2002. 6(4): p. 393 - 423.

26. Liu, H. and H. Motoda, Feature selection for knowledge discovery and data mining. 1998, Boston: Kluwer Academic.

27. Burges, C.J.C., A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 1998. 2: p. 121-167.

28. Breiman, L., et al., Classification and regression trees. 1984, Belmont, Calif.: Wadsworth International Group.

29. Quinlan, J.R., C4.5: programs for machine learning. 1993, San Mateo, Calif.: Morgan Kaufmann.

C4.5 is a classic package with decision tree classification method.

30. Han, J. and M. Kamber, Data mining: concepts and techniques. 2001, San Francisco: Morgan Kaufmann.

31. Mitchell, T.M., Machine Learning. 1997, New York: McGraw-Hill.

32. Witten, I.H. and E. Frank, Data mining: practical machine learning tools and techniques with Java implementations. 1999, San Francisco: Morgan Kaufmann.

The authors are the main contributors of Weka, the probably best widely-used open source data mining package.

33. Friedman, J., T. Hastie, and R. Tibshirani, The elements of statistical learning: data mining, inference, and prediction. 2001, New York: Springer.

34. Bishop, C.M., Neural networks for pattern recognition. 1995, Oxford: Clarendon Press.