CS3710 Probabilistic graphical models:
Readings.
Instructor: Milos
Hauskrecht
Computer Science Department
5329 Sennott Square
phone: x4-8845
e-mail: milos_at_cs.pitt.edu
Covered topics
Graphical models: basics
E. Charniak
Bayesian networks without fears.
Kevin Murphy. An Introduction to Graphical
models.
Michael I. Jordan Graphical models. Statistical Science (Special Issue on Bayesian
Statistics), 19, 140-155, 2004.
Independences in graphical models
- Judea Pearl. Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann Publishers, Palo Alto, CA, USA, 1988.
Inferences: complexities
- G. F. Cooper. The computational complexity of probabilistic inference using Bayesian belief networks.
Artificial Intelligence, vol. 42, no. 2-3, pages 393--405, March 1990.
- P. Dagum. M. Luby. Approximating probabilistic inference in Bayesian belief networks is NP-hard. Artificial Intelligence, vol. 60, pages 141-- 153, 1993.
- D. Chickering. Learning bayesian networks is np-complete .
Proceedings of AI and Statistics, 1995.
Markov Random Fields
Variable elimination
- R. Dechter. Bucket elimination.
Pearl's algorithm
- Judea Pearl. Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann Publishers, Palo Alto, CA, USA, 1988.
Joint tree algorithm
- Lauritzen, S. and D. Spiegelhalter, Local computations with probabilities on graphical structures and their applications to expert systems,
J. Royal Statistical Society B, 50, pp. 157-224, 1988.
Recursive decomposition
- G. Cooper. Bayesian Belief-Network Inference Using Recursive Decomposition.Technical Report, SMI-90-0291, Stanford University, 1992.
- Adnan Darwiche. Recursive conditioning. Arificial Intelligence Journal,
2000.
Local probability models and inferences with such models
Monte Carlo approximations
Adaptive importance sampling
Bethe and Kikuchi Approximations
Mean field /variational approximations
- Jordan, M., Ghaharamani, Z. Jaakkola, T., and Saul, L. An
introduction to variational methods for graphical models. In
M. I. Jordan (Ed.), Learning in Graphical Models, 1998.
- Wim Wiegerinck.
Variational Approximations between Mean Field Theory and the Junction
Tree Algorithm.
Proceedings of Uncertainty in AI, pages 626--633. Morgan Kaufmann, 2000.
- Hilbert J. Kappen, Wim J. Wiegerinck. Mean field theory for graphical
models. In M. Opper and D. Saad, editors, Adavanced Mean Field Theory
-- Theory and Practice, chapter 4, pages 37--49. MIT Press, 2001.
- Z. Ghahramani and M. J. Beal.
Graphical Models and Variational Methods. In M. Opper and D. Saad (eds.), Advanced Mean Field Methods --- Theory and Practice, MIT Press, 2001.
Learning graphical models
Expectation maximization
Structural EM