6.438 Algorithms for Estimation & Inference
Fall 2008
Carl Friedrich Gauss and Andrey Andreyevich Markov
Instructors: William T Freeman, Gregory W Wornell
TAs: Hyun Sung Chang, Emily Beth Fox, Charles Swannack
Lecture:
TR9.30-11
(32-124)
Recitation 1: F10-11
(36-153)
Recitation 2: F11-12
(36-153)
Information:
Introduction to models and algorithms for efficient estimation and inference. Linear estimation via inner product spaces, Karhunen-Loeve expansions, Cholesky decompositions. Linear state-space models, Kalman filtering, RTS algorithm. Asymptotics, non-linear extensions and particle filtering. Autoregressive modeling and linear prediction; RLS and Levinson algorithms. Hidden Markov models; forward-backward, Viterbi, and Baum-Welch algorithms. Introduction to graphical models and belief propagation; sum-product, min-sum, and junction tree algorithms. Selected special topics such as variational methods and mean field theory.
Announcements
6.438 Quizzes and Grades
After a rather intense term you are now officially 6.438 alumni...congratulations on all your hard work!You can pick up your graded quizzes with final grade assignments (in person) from Tricia, our course assistant, in 36-677. This week, Tricia is in all day Wednesday, Thursday morning, but not Friday. She is also in Monday and Tuesday mornings of next week.
Also, solutions to the quiz, and the quiz score histogram are now posted on the web site.
Enjoy the holidays, and keep in touch.
Greg, Bill, Hyun, Emily, and Charles
Announced on 16 December 2008 7:26 p.m. by Gregory Wornell
please fill out course evaluation survey, at https://sixweb.mit.edu, if you haven't alread
Have a good holiday and break!
--Bill (and Greg)
Announced on 13 December 2008 11:15 p.m. by William Freeman
Problem Set 9 Changes and Comments
Dear class,
Please make a note of the following 3 important points regarding Problem Set 9:
(1) In Problem 9.1, there is a typo. The compatibility functions over variables taking on the same value should be 10, not 1. Similarly the compatibility functions for the pairs of random variables taking on opposite values should be 1, not 10. That is,
\psi_{x,y}(0,0) = \psi_{x,y}(1,1) = 10 for all x and y in
{a,b,c}
\psi_{x,y}(1,0) = \psi_{x,y}(0,1) = 1 for all x and y in
{a,b,c}.
(2) Loopy belief propagation will be covered in Thursday's lecture. However, if you would like to get started on this part of the problem set, the following statement will prove sufficient for the questions asked. Recall that our "simple to program" BP scheme (in which messages are initialized to 1 and all nodes continually pass and receive messages) did not rely on any type of leaf-to-root or root-to-leaf processing. Therefore, this scheme can be run on graphs with loops. Whether or not the resulting marginals calculated by this "loopy BP" correspond to the correct node marginals will be discussed more on Thursday, but is irrelevant for the problem set.
(3) To give you a little more time to complete the problem set, we have extended the problem set due date by one day, i.e., to FRIDAY, DECEMBER 5th, the last day allowable by the Institute. Please turn your problem set in at recitation.
Thanks,
6.438 staff
Announced on 03 December 2008 11:31 a.m. by Hyun Sung Chang
TA Office Hours for the remainder of term
Wed. 11/26: NO OFFICE HOURS
Mon. 12/1: 5pm -- 6:30pm
Wed. 12/3: 4pm -- 5:30pm
Mon. 12/8: 5pm -- 6:30pm
Announced on 25 November 2008 12:54 p.m. by Hyun Sung Chang
Problem 7.8 (f) Clarification
All,
In Problem 7.8(f), you should know what chordal graphs are. So let me give its definition.
Definition:
A graph is chordal (triangulated) if every cycle of length 4 or
more nodes has a "chord" (i.e. an edge between 2
non-adjacent nodes in cycle).
You may refer to http://en.wikipedia.org/wiki/Chordal_graph for more details.
Best,
Hyun
Announced on 13 November 2008 2:58 p.m. by Hyun Sung Chang
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