The following excerpt from “A Brief Introduction to Graphical Models and Bayesian Networks” by Kevin Murphy.
Books
In reverse chronological order.
- Daphne Koller and Nir Friedman, “Probabilistic graphical models: principles and techniques”, MIT Press 2009
- Adnan Darwiche, “Modeling and reasoning with Bayesian networks”, Cambridge 2009
- F. V. Jensen. “Bayesian Networks and Decision Graphs”. Springer. 2001.
Probably the best introductory book available. - D. Edwards. “Introduction to Graphical Modelling”, 2nd ed. Springer-Verlag. 2000.
Good treatment of undirected graphical models from a statistical perspective. - J. Pearl. “Causality”. Cambridge. 2000.
The definitive book on using causal DAG modeling. - R. G. Cowell, A. P. Dawid, S. L. Lauritzen and D. J. Spiegelhalter. “Probabilistic Networks and Expert Systems”. Springer-Verlag. 1999.
Probably the best book available, although the treatment is restricted to exact inference. - M. I. Jordan (ed). “Learning in Graphical Models”. MIT Press. 1998.
Loose collection of papers on machine learning, many related to graphical models. One of the few books to discuss approximate inference. - B. Frey. “Graphical models for machine learning and digital communication”, MIT Press. 1998.
Discusses pattern recognition and turbocodes using (directed) graphical models. - E. Castillo and J. M. Gutierrez and A. S. Hadi. “Expert systems and probabilistic network models”. Springer-Verlag, 1997.
A Spanish version is available online for free. - F. Jensen. “An introduction to Bayesian Networks”. UCL Press. 1996. Out of print.
Superceded by his 2001 book. - S. Lauritzen. “Graphical Models”, Oxford. 1996.
The definitive mathematical exposition of the theory of graphical models. - S. Russell and P. Norvig. “Artificial Intelligence: A Modern Approach”. Prentice Hall. 1995.
Popular undergraduate textbook that includes a readable chapter on directed graphical models. - J. Whittaker. “Graphical Models in Applied Multivariate Statistics”, Wiley. 1990.
This is the first book published on graphical modelling from a statistics perspective. - 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.
The book that got it all started! A very insightful book, still relevant today.
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.
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