Recommended reading

These papers are what I consider as some of the more readable entry points to the field.

• Books
  • E. Castillo and J. M. Gutierrez and A. S. Hadi, 1997. "Expert systems and probabilistic network models". Springer-Verlag.

  • D. Edwards, 1995. "Introduction to graphical modelling", Springer.

  • B. Frey, 1998. "Graphical models for machine learning and digital communication", MIT Press.

  • F. Jensen, 1996. "An introduction to Bayesian Networks". UCL Press.

  • M. I. Jordan (ed), 1998. "Learning in Graphical Models", Kluwer Academic Press.

  • S. Lauritzen, 1996. "Graphical Models", Oxford.

  • J. Pearl, 1988. "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference", Morgan Kaufmann.

  • S. Russell and P. Norvig, 1995. "Artificial Intelligence: A Modern Approach". Prentice Hall.

  • J. Whittaker, 1990. "Graphical Models in Applied Multivariate Statistics", Wiley.

• Inference
  • U. Kjaerulff, 1990. "Triangulation of graphs - algorithms giving small total state space", Tech report R-90-09, Dept. of Math. and Comp. Sci., Aalborg Univ., Denmark.

  • M. Peot and R. Shachter, 1991. "Fusion and propogation with multiple observations in belief networks", Artificial Intelligence, 48:299-318.

  • S. Shafer and S. Lauritzen, 1996. "Computing Marginals Using Local Computation," Working Paper No. 267, School of Business, University of Kansas.

• Learning
  • W. L. Buntine, 1994. "Operations for Learning with Graphical Models", J. AI Research, 159-225.

  • N. Friedman, 1998. "The Bayesian Structural EM Algorithm", Proc. UAI.

  • D. Heckerman, 1996."A tutorial on learning with Bayesian networks". Microsoft Research tech. report, MSR-TR-95-06.

  • P. Krause, 1998. "Learning probabilistic networks", Philips Research Labs tech. report.

• DBNs
  • N. Friedman, K. Murphy, and S. Russell, 1998. "Learning the Structure of DPNs", UAI.

  • Z. Ghahramani, 1997. "Learning Dynamic Bayesian Networks", to appear in "Adaptive Processing of Temporal Information. Lecture Notes in Artificial Intelligence", ed., C.L. Giles and M. Gori, Springer-Verlag.

  • L. R. Rabiner, 1989. "A Tutorial in Hidden Markov Models and Selected Applications in Speech Recognition", Proc. of the IEEE, 77(2):257-286.

  • P. Smyth and D. Heckerman and M. I. Jordan, 1996. "Probabilistic Independence Networks for Hidden Markov Probability Models". Microsoft Research Tech. Report, MSR-TR-96-03.

• Hybrid (discrete/continuous) networks
  • K. Murphy, 1998. "Switching Kalman Filter models", in preparation.

  • Z. Ghahramani and G. Hinton, 1998. "Switching State-Space Models". Submitted.

  • K. P. Murphy, 1998. "Inference and Learning in Hybrid Bayesian Networks" Tech Report No. 990, U.C. Berkeley, Dept. Comp. Sci.

  • S. Roweis and Z. Ghahramani, 1997. "A Unifying Review of Linear Gaussian Models", submitted.