Markovia Reading Group at UW and Intel Seattle Lab

This quarter we will focus on the approximate inference for the graphical model and two specific models, hidden Markov model and Kalman filters.

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Thursdays 12:00-1:00pm in room AC 203.

An Introduction to Graphical Models, Michael I. Jordan and Christopher M. Bishop, preprint

Hybrid Bayesian Networks for Reasoning about Complex Systems, Uri Lerner, Ph.D. thesis, http://robotics.stanford.edu/~uri/Papers/thesis.ps.gz

Week	Topic	Paper/Chapter	Leader
1	Discuss the plan for the quarter		--
2	Hidden Markov Model (HMM)	Chap. 12 of Jordon’s book	Julie Letchner and Benson Limketkai
3	Talk by Drew Bagnell at Intel Seattle Lab
4	EM algorithm for HMM	Chap. 10 of Jordon’s book	Lin Liao
5	Gaussians for Graphical Model	Chap. 3 of Lerner’s thesis (optional) Chap. 13 of Jordon’s book	Colin Zheng
6	Gaussians for Graphical Model (cont’d)		Colin Zheng
7	Kalman Filtering and Smoothing	Chap. 15 of Jordon’s book	Jonathan Ko
8	Variational method	Chap. 22 of Jordon’s book	Karthik Gopalratnam
9	Variational method (cont'd)	Tutorial on variational approximation methods	Sumit Basu
10	Variational method (cont'd)		Sumit Basu