Computerized Adaptive Testing

References for learning Bayesian Networks and more general graph models

Books

• 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  Superceded by his 2001 book.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. 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.

posted by Shunkai Fu @ 9:08 PM