Stat 202 C

Markov Chain Monte Carlo and Optimization

MWF 11:00-11:50 am, Spring 2010, Maths/Sciences 5128.
 
www.stat.ucla.edu/~yuille/Courses/UCLA/Stat_231/Stat_231.html.
 

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Course Description

This course describes MCMC sampling techniques with emphasis on optimization and statistical estimation. Topics covered include Gibbs samplers, Metropolis-Hastings, importance sampling, and simulated annealing. In addition, the course covers alternative optimization techniques including Newton-Raphson, dynamic programming, belief propagation, and variational methods.

Textbook

• Jun S. Liu. "Monte Carlo Strategies in Scientific Computing", Springer 2001. [Required]
• Additional Reading: Introduction to Monte Carlo Methods. D.J.C. Mackay.
• C.R. Robert and G. Casella. Monte Carlo Statistical Methods. Optional Reading.
  

Instructors

• Prof. Alan Yuille, yuille@stat.ucla.edu, 310-267-5383, office Math Sciences 8967. Office Hours: Wed 4:00-5:00 pm and by request.

Grading Plan: 4 units, letter grades. 4 homework assignments. 1 final exam.
Homework 1:
Homework 2:
Homework 3:
Homework 4:

Tentative Schedule.

Lecture	Date	Topics	Reading Materials	Handouts	Roberts & Casella
1	3-29	Introduction to Sampling and Monte Carlo: Issues, Applications, and Examples.	Ch 1	2010lecture1.pdf	Ch 2
2	3-31	Basic Monte Carlo: Inversion Method, Gaussians, Mixtures	Ch 2.1	2010lecture2.pdf	Ch 2.2
3	4-2	Basic Monte Carlo: Rejection Methods.	Ch 2.2	2010lecture3.pdf	Ch 2.3, 2.4
4	4-5	Basic Monte Carlo: Rao-Blackwellization and Exact Methods.	Ch 2	2010lecture4.pdf	Ch  4.2
5	4-7	Basic Monte Carlo: Importance Sampling	Ch 2.5	2010lecture5.pdf	Ch 3.1-3.3
6	4-9	Basic Monte Carlo: Importance Sampling,	Ch 2.5	2010lecture6.pdf	Ch 3.1-3.3
7	4-12	Basic Monte Carlo Weighted Sampling.	Ch 2.6	2010lecture7.pdf	Ch 3.1-3.3
8	4-14	Exact Monte Carlo Dynamic Programming	Ch 2.4	2010lecture8.pdf
9	4-16	Exact Monte Carlo (cont) Dynamic Programming	Ch 2.4	Same as previous lecture
10	4-19	Structured Probability Distributions Examples.	GYtics.pdf	2010lecture9.pdf
11	4-21	Kalman Filters: Filtering and Tracking Examples.	Ch 3.2	2010lecture10.pdf
12	4-23	Particle (Boostrap) Filters: (Cont) Filtering and Tracking Examples.	Ch 3.2-3.3, 4.5	2010lecture11.pdf
13	4-26	Particle (Boostrap) Filters: (Cont) Filtering and Tracking Examples	Ch 3.2-3.3, 4.5	2010lecture12.pdf
14	4-28	Particle (Boostrap) Filters: (Cont) Filtering and Tracking Examples	Ch 3.2-3.3, 4.5	2010lecture13.pdf
15	4-30	Sequential Importance Sampling Saw Examples	Ch 3.2-3.3, 4.5	2010lecture14.pdf
16	5-3	Markov Chain Monte Carlo: Introduction	Ch 5.0, 5.1	2010lecture15.pdf
17	5-5	Markov Chain Monte Carlo: Example: Ising Model	Ch 5.2, 5.3	Same as previous lecture
18	5-7	Markov Chain Monte Carlo: Metropolis-Hastings and Gibbs Sampler.	Ch 5.3	2010lecture16.pdf
19	5-10	Metropolis-Hastings Examples	Guest Lecture: Prof. Q. Zhou
20	5-12	Physical Methods: Hybrid Monte Carlo	Ch 9.1-9.4	2010lecture17.pdf
21	5-14	Data Augmentation	Ch 6.4	Same as lecture 16.pdf
22	5-17	Data Augmentation and EM	Appendix A4.	2010lecture18.pdf
23	5-19	Reversible Jumps and Multiple Try MH:	Ch 5.5, 5.6	2010lecture19.pdf
24	5-21	Swendsen-Wang	Ch 7.1,7.2,7.4	See lecture 18.
25	5-24	Genetic Algorithms:	GA Handout	2010lecture20.pdf
26	5-26	Genetic Algorithms (Cont)::	Previous lecture	Previous Lecture
27	5-28	Convergence and Review
	5-31	Memorial Day Holiday		2010lecture21.pdf
28	6-2	Deterministic Methods	Reading List
29	6-4	Assorted Topics: