last updated: Tue Feb, 26th 2003
To suggest new papers or to join the group please send email to ash@media.mit.edu or add yourself to the group alias: StatLearn@media.mit.edu
[Feb 11, Ashish] Kevin Murphy, Dynamic Bayesian Networks, draft for chapter in Jordan's book on Probabilistic Graphical Models.
[Feb 25, Raul] Basic Concepts: Junction Tree Algorithm (Chapter 16 of Jordan's book on Graphical Models). Also review of Kalman filter (Chapter 14). Contact ash@media.mit.edu to obtain a copy.
[Mar 5, Ashish] Kalman Filters (EKF, UKF): T. Lefebvre and H. Bruyninckx and J. De Schutter. Kalman filters for nonlinear systems: a comparison of performance. http://citeseer.nj.nec.com/article/lefebvre01kalman.html. Internal Report 01R033, Department of Mechanical Engineering, Katholieke Universiteit Leuven, Belgium, Oct 2001.
[Mar 12, Niloy] Sequential Monte-Carlo (Particle Filters): J. S. Liu and R. Chen. Sequential Monte Carlo methods for dynamic systems. Journal of American Statistical Association, Vol: 93(443):1032-1044, 1998. Optional (Background on MCMC): J. S. Liu. Markov chain Monte Carlo and related topics. http://citeseer.nj.nec.com/184432.html
[Mar 26, Niloy] Introduction to Monte Carlo Methods, D.J.C Mackay. http://www.inference.phy.cam.ac.uk/mackay/BayesMC.html and Arulampalam et al. A Tutorial on Particle Filters for On-line Non-linear/Non-Gaussian Bayesian Tracking. In IEEE Transactions on Signal Processing.
[Apr 3, Ashish] Assumed Density Filtering: M. Opper and O. Winther. A Bayesian approach to online learning, in Online learning in Neural Networks. Cambridge University Press 1999. Optional: T. P. Minka Chap 3 in A Family of algorithms for approximate Bayesian inference and X. Boyen and D. Koller. Tractable inference for complex stochastic processes. Uncertainty in AI, 1998.
Belief Propagation
Expectation Popagation
Variational Methods