Stat
202
C
Markov
Chain
Monte Carlo
and Optimization
MWF 11:00-11:50 am, Spring 2011, PAB 2748.
www.stat.ucla.edu/~yuille/Courses/UCLA/Stat_231/Stat_231.html.
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.
Note: course will be very similar to Spring 2010, there will be small
changes to the lecture notes.
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
|
3-28
|
Introduction to Sampling and Monte Carlo:
Issues, Applications, and Examples.
|
Ch
1
|
2011lecture1.pdf
|
Ch 2
|
|
2
|
3-30
|
Basic Monte Carlo:
Inversion Method, Gaussians, Mixtures
|
Ch
2.1
|
2011lecture2.pdf
|
Ch 2.2
|
3
|
4-1
|
Basic Monte Carlo:
Rejection Methods.
|
Ch
2.2
|
2011lecture3.pdf
|
Ch 2.3, 2.4
|
|
4
|
4-4
|
Basic Monte Carlo:
Rao-Blackwellization and Exact Methods.
|
Ch
2
|
2011lecture4.pdf
|
Ch 4.2
|
|
5
|
4-6
|
Basic Monte Carlo:
Importance Sampling
|
Ch 2.5
|
2011lecture5.pdf
|
Ch 3.1-3.3
|
6
|
4-8
|
Basic Monte Carlo:
Importance Sampling,
|
Ch
2.5
|
2011lecture6.pdf
|
Ch 3.1-3.3 |
|
7
|
4-11
|
Basic Monte Carlo
Weighted Sampling.
|
Ch
2.6
|
2011lecture7.pdf
|
Ch 3.1-3.3 |
|
8
|
4-13
|
Exact Monte Carlo
Dynamic Programming
|
Ch 2.4
|
2011lecture8.pdf
|
|
9
|
4-15
|
Exact Monte Carlo (cont)
Dynamic Programming
|
Ch
2.4
|
2011lecture9.pdf
|
|
|
10
|
4-18
|
Structured Probability Distributions
Examples.
|
GYtics.pdf
|
2011lecture10.pdf
|
|
|
11
|
4-20
|
Kalman Filters:
Filtering and Tracking Examples.
|
Ch
3.2 |
2011lecture11.pdf
|
|
12
|
4-22
|
Particle (Boostrap) Filters: (Cont)
Filtering and Tracking Examples.
|
Ch 3.2-3.3,
4.5
|
2011lecture12.pdf
|
|
|
13
|
4-25
|
Particle (Boostrap) Filters: (Cont)
Filtering and Tracking Examples
|
Ch 3.2-3.3,
4.5 |
2011lecture13.pdf
|
|
|
14
|
4-27
|
Particle (Boostrap) Filters: (Cont)
Filtering and Tracking Examples
|
Ch 3.2-3.3,
4.5
|
2011lecture14.pdf
|
|
15
|
4-29
|
Sequential Importance Sampling
Saw Examples
|
Ch 3.2-3.3,
4.5 |
2011lecture15.pdf
|
|
|
16
|
5-2
|
Markov Chain Monte Carlo:
Introduction
|
Ch
5.0,
5.1
|
2011lecture16.pdf
|
|
|
17
|
5-4
|
Markov
Chain
Monte Carlo:
Example:
Ising
Model
|
Ch
5.2,
5.3
|
2011lecture17.pdf
|
|
18
|
5-6
|
Markov Chain Monte Carlo:
Metropolis-Hastings
|
Ch
5.3
|
2011lecture18.pdf
|
|
|
19
|
5-9
|
Gibbs Sampler
Data Augmentation
|
Ch
6.1,6.2
|
2011lecture19.pdf
|
|
|
20
|
5-11
|
Gibbs Sampler
Data Augmentation (Cont)
|
Ch 6.3
|
Previous Lecture
|
|
21
|
5-13
|
Reversible Jumps and Multiple Try MH
Hybrid Monte Carlo
|
Ch
5.5,5.6
|
2011lecture20.pdf
|
|
|
22
|
5-16 |
Convergence of MCMC
|
Ch
12 |
2011lecture21.pdf
|
|
23
|
5-18
|
Hybrid Monte Carlo
|
Ch 9.1,9.2,9.3 |
2011lecture22.pdf
|
|
24
|
5-20
|
Genetic Algorithms
|
GA
Handout
|
2011lecture23.pdf
|
ga_tutorial.pdf
|
25
|
5-23
|
Population Methods and Simulated Annealing
|
Ch 11.1-11.3 |
Previous Lecture
|
|
|
26
|
5-25
|
Swenson-Wang
|
Ch
7
|
2011lecture24.pdf
|
|
27
|
5-27
|
Deterministic Methods
|
|
2010lecture25.pdf
|
|
|
5-30
|
Memorial Day Holiday
|
|
|
|
|
28
|
6-1
|
Deterministic Methods
|
Reading
List
|
Previous Lecture
|
|
|
29
|
6-3
|
Overview:
|
|
2010lecture26.pdf
|
|