Readings

• Introduction to Probabilistic Graphical Models , Michael Jordan
• Bayesian networks and Beyond, Daphne Koller and Nir Friedman

Lecture 1 (13 Sep 04)

• Matlab tutorials
• Brief introduction to graphical models, Kevin Murphy, web page
• Chapter 2 of Koller & Friedman (Foundations).
• Chapter 2 of Jordan (Conditional independence and factorization).
• Chapter 13 of Russell & Norvig (Uncertainty), "AI: A modern approach", 2nd edition.

Lecture 2 (15 Sep 04)

• Chapter 3 of Koller & Friedman (The Bayesian network representation). Read pp 67--101.

Lecture 3 (20 Sep 04)

• Chapter 5 of Koller & Friedman (Undirected graphical models).

Lecture 4 (22 Sep 04)

• Chapter 4 of Koller & Friedman (Local probabilistic models).

Lecture 5 (27 Sep 04)

• Chapter 6 of Koller & Friedman (Inference with graphical models)
• Chapter 7 of Koller & Friedman (Exact inference: variable elimination)

Lecture 6 (29 Sep 04)

• "Bucket Elimination: A Unifying Framework for Probabilistic Inference", Rina Dechter, UAI 1996. Extended version appears in "Learning in Graphical Models", ed. M. Jordan.

Lecture 7 (4 Oct 04)

• Chapter 8 of Koller & Friedman (Exact inference: clique trees)
• Cecil Huang and Adnan Darwiche. Inference in belief networks: A procedural guide. International Journal of Approximate Reasoning, 15(3):225-263, October, 1996.

Lecture 8 (6 Oct 04)

Lecture 9 (Mon 18 Oct)

• Chapter 12 of Koller & Friedman (Learning: introduction)
• Chapter 13 of Koller & Friedman (Parameter estimation)

Lecture 10 (Wed 20 Oct)

• Chapter 5 of Jordan (statistical concepts)
• Chapter 8 of Jordan (exponential family)
• Chapter 9 of Jordan (completely observed graphical models)
• Optional: A tutorial on HMMs and Selected Applications in Speech Recognition, Rabiner, Proc IEEE, 1989 77(2):257--286

Lecture 11 (Mon 25 Oct)

• Chapter 15 of Koller & Friedman (partially observed data)
• Optional: Matrix facts cheat sheet
• Matrix cook book
• Optional: A hierarchical dirichlet language model. David J. C. McKay and Linda C. Bauman Peto. Natural Language Engineering, 1(3):289--307, 1995.
• Optional: Bayesian extension to the language model for ad hoc information retrieval, Hugo Zaragoza, Djoerd Hiemstra, Michael Tipping, ACM SIGIR 2003
• Optional: Tutorials on Bayesian methods by Tom Minka

Lecture 12 (Wed 27 Oct)

• Chapter 10 of Jordan (mixtures and conditional mixtures)
• Chapter 11 of Jordan (EM)
• Chapter 12 of Jordan (HMMs)

Lecture 13 (Mon 1 Nov)

Lecture 14 (Wed 3 Nov)

• Chapter 19 of Jordan (Features, maxent)
• Chapter 20 of Jordan (iterative scaling)
• Chapter 14 of Koller & Friedman (structure learning)

Lecture 15 (Mon 8 Nov)

• Optional: Brief introduction to Bayesian machine learning, Z. Ghahramani
• Optional. For a description of the debate between Bayesians and frequentists see Chapter 37 of David MacKay's excellent textbook.
• Optional: From P Values to Bayesian Statistics, It's All in the Numbers, The Scientist, Apr. 26, 2004
• P values: what they are and what they are not, Mark Schervish, The American Statistician, 1996
• Reading list on Bayesian methods by Tom Griffiths
• Pathologies of orthodox statistics, Tom Minka tech report 2001
• Nuances of probability theory, Tom Minka tech report 2001
• Bayesian jokes

Lecture 16 (Wed 10 Nov)

• Optional: Tutorial on approximate inference, Frey and Jojic, PAMI 2004

• Jordan ch 13 (Multivariate Gaussian)
• Jordan ch 14 (Factor analysis)
• Jordan ch 15 (Kalman filtering and smoothing)
• A technique for painless derivation of kalman filtering recursions, A. Cemgil, Tech Report, U. Nijmegen, 2001
• From Hidden Markov Models to Linear Dynamical Systems, MIT Media Lab Tech report 531, Tom Minka, 1999
• A Unifying Review of Linear Gaussian Models, Sam Roweis & Zoubin Ghahramani. Neural Computation 11(2) (1999) pp.305-345
• An introduction to factor graphs, H.-A. Loeliger, IEEE Signal Proc. Mag, Jan. 2004, pp. 28-41.
• Least squares and Kalman filtering on Forney graphs, H.-A. Loeliger, in Codes, Graphs, and Systems, (festschrift in honour of David Forney on the occasion of his 60th birthday), R.E. Blahut and R. Koetter, eds., Kluwer, 2002, pp. 113-135.

Lecture 19 (Wed 24 Nov)

• Chapter 9 of Koller (particle based approximations)
• Optional: MCMC and related topics, tutorial by Jun Liu, 1999

Lecture 20 (Mon 29 Nov)

• Understanding Belief Propagation and Its Generalizations", Yedidia, J.S.; Freeman, W.T.; Weiss, Y., Exploring Artificial Intelligence in the New Millennium, Chap. 8, pp. 239-236, January 2003 (Science & Technology Books)

Applications

Molecular Biology

• Inferring Cellular Networks using PGMs, N. Friedman, Science 303: 799--805, 6 Feb 2004.
• Graphical models for genetic analyses. S. L. Lauritzen and N. A. Sheehan. Statistical Science, 18, 489-514, 2003.

Computer vision

Markov random fields and images, P. Perez. CWI Quarterly, 11(4):413-437, 1998.
• Tutorial on approximate inference, Frey and Jojic, PAMI 2004

• Kersten, D., Mamassian, P., & Yuille, A. (2004). Object perception as Bayesian Inference. Annual Review of Psychology, 55, 271-304.
• Kersten, D., & Yuille, A. (2003). Bayesian models of object perception. Current Opinion in Neurobiology, 13(2)

Legal reasoning

• Probability and Statistics in the Law, A P Dawid, UCL TR 204

Popular press

• LA times article (10/28/96) about Bayes nets.
• Economist article (3/22/01) about Microsoft's application of Bayes nets/ decision theory in Mobile Manager.
• Economist article (30 Sep 2000) about Bayesian methods for clinical trials