Tool box For Graphical Model and Bayesian Network: Tetrad Bayesian Network KM BN Matlab Google List
- F. V. Jensen. Bayesian Networks and Decision Graphs.Springer. 2001.
- D. Edwards. Introduction to Graphical Modelling, 2nd ed. Springer-Verlag. 2000.
- R. G. Cowell, A. P. Dawid, S. L. Lauritzen and D. J. Spiegelhalter. Probabilistic Networks and Expert Systems. Springer-Verlag. 1999.
- M. I. Jordan (ed). Learning in Graphical Models. MIT Press. 1998. Learning in Graphical Models
- B. Frey. Graphical models for machine learning and digital communication", MIT Press. 1998.
- E. Castillo and J. M. Gutierrez and A. S. Hadi. Expert systems and probabilistic network models. Springer-Verlag,1997.
- S. Lauritzen. Graphical Models, Oxford. 1996.
- J. Whittaker. Graphical Models in Applied Multivariate Statistics, Wiley. 1990.
- R. Neapoliton. Probabilistic Reasoning in Expert Systems. John Wiley & Sons. 1990.
- J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. 1988.
Causality
- J. Pearl. Causality Cambridge. 2000.
- P. Spirits, C. Glymour and Richard Scheines Causation, Prediction and Search; Second Editio
Bayesian Theory
- J. Bernardo and A. Smith Bayesian Theory.
Decision Analysis Reading List
Review articles
- P. Smyth, 1998. "Belief networks, hidden Markov models, and Markov random fields: a unifying view", Pattern Recognition Letters.
- E. Charniak, 1991. "Bayesian Networks without Tears", AI magazine.
- Sam Roweis & Zoubin Ghahramani, 1999. A Unifying Review of Linear Gaussian Models, Neural Computation 11(2) (1999) pp.305-345
Exact Inference
- C. Huang and A. Darwiche, 1996. "Inference in Belief Networks: A procedural guide", Intl. J. Approximate Reasoning, 15(3):225-263.
- R. McEliece and S. M. Aji, 2000. The Generalized Distributive Law, IEEE Trans. Inform. Theory, vol. 46, no. 2 (March 2000), pp. 325--343.
- F. Kschischang, B. Frey and H. Loeliger, 2001. Factor graphs and the sum product algorithm, IEEE Transactions on Information Theory, February, 2001.
- M. Peot and R. Shachter, 1991. "Fusion and propogation with multiple observations in belief networks", Artificial Intelligence, 48:299-318.
Approximate Inference
- M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul, 1997. "An introduction to variational methods for graphical models."
- D. MacKay, 1998. "An introduction to Monte Carlo methods".
- T. Jaakkola and M. Jordan, 1998. "Variational probabilistic inference and the QMR-DT database"
- T. Minka 2002 A family of algorithms for approximate inference
Learning
- W. L. Buntine, 1994. "Operations for Learning with Graphical Models", J. AI Research, 159--225.
- D. Heckerman, 1996. "A tutorial on learning with Bayesian networks", Microsoft Research tech. report, MSR-TR-95-06.
DBNs
- L. R. Rabiner, 1989. "A Tutorial in Hidden Markov Models and Selected Applications in Speech Recognition", Proc. of the IEEE, 77(2):257--286.
- Z. Ghahramani, 1998. Learning Dynamic Bayesian Networks 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.
- T. Minka Bayesian Inference in Dynamic Models: an overview