an introduction to graphical models

[am]

K.P.Murphy "An Introduction to Graphical Models" 2001
http://www.cs.ubc.ca/~murphyk/Papers/intro_gm.pdf
supplimentary stuff is here:
http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html
c.f. literature list here:
http://am.livejournal.com/147602.html
and links list here:
http://www.cs.cornell.edu/Courses/cs664/2005fa/links.htm
 two other books:
Stan Z. Li " Markov Random Field Modeling in Computer Vision"
 (Springer,  1st edition 1995,  2nd edition 2001,  3rd edition 2009)
http://www.cbsr.ia.ac.cn/users/szli/MRF_Book/book.html#toc
Havard Rue, Leonhard Held "Gaussian Markov Random Fields:Theory
and Applications" (Monographs on Statistics and Applied Probability)

Applications to biology
This is one of the hottest areas. For a review, see

• Inferring cellular networks using probabilistic graphical models Science, Nir Friedman, v303 p799, 6 Feb 2004.

Recommended introductory reading
Books
In reverse chronological order (bold means particularly recommended)

•   F.V. Jensen, T.D. Nielsen, "Bayesian Networks and Decision Graphs"
(Information Science and Statistics) Springer. 2007-06-06, ISBN: 0387682813, 448 p.

• 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  Superceded by his 2001(2007) books.
• 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. 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"

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.
  
  some applications in machine learning
  
  
• Gilks W.R., Richardson S. and Spiegelhalter D.J.
  "Markov Chain Monte Carlo in Practice". Chapman & Hall/CRC, 1996.
  C.Andrieu et al., "An Introduction to MCMC for Machine Learning", 2003
• L. Getoor, N. Friedman, D. Koller, A. Pfeffer, and B. Taskar (2007)
  "Probabilistic Relational Models" In L.Getoor, B.Taskar,
  editors, Introduction to Statistical Relational Learning.
  (with initially unknown object types, predicates, dependencies)
   Lise Getoor; Ben Taskar " Introduction to statistical relational learning"
  Cambridge, Mass.  MIT Press, (2007). 586 p.
• Dirichlet process prior allowing models to grow to explain data.
  http://www.psy.cmu.edu/~ckemp/    http://web.mit.edu/vkm/www/
  Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T., and Ueda, N. (2006).
  "Learning systems of concepts with an infinite relational model"
  In Proceedings of the AAAI Conference on Ar tificial Intelligence, vol.21.
  Roy, D. M., Kemp, C., Mansinghka, V., and Tenenbaum, J. B. (2007).
  "Learning annotated hierarchies from relational data"
  In Advances in Neural Information Processing Systems, vol.19.

some technicalities (must read)

• 
  Kschischang, F.R.; Frey, B.J.; Loeliger, H.-A. (2001) "Factor Graphs and the Sum-Product Algorithm" IEEE Transactions on Information Theory 47 (2): 498-519. http://www.psi.utoronto.ca/pubs/2001/frey2001factor.pdf
• 
  Dmitry M. Malioutov, Jason K. Johnson, Alan S. Willsky "Walk-Sums and Belief
  Propagation in Gaussian Graphical Models" JMLR; 7(Oct), pp.:2031--2064, 2006.
  http://jmlr.csail.mit.edu/papers/v7/malioutov06a.html

Machine Learning journal club and lectures:
http://www.cs.utexas.edu/~ml/dm/