Statistics 9250
Statistical Computation and Simulation
Winter 2008
Instructor
Office
Email
Webpage
Hours
Marco A. R. Ferreira
209-E Middlebush Hall (884-8568)
ferreiram AT missouri.edu
www.stat.missouri.edu/~ferreiram
Monday and Wednesday from 3 - 4pm or by appointment
Text
Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference
(2nd ed), Gamerman and Lopes
References
Monte Carlo Statistical Methods (2nd ed), Robert and Casella
Tools for Statistical Inference (3rd ed), Tanner
Modern Applied Statistics with S-Plus (3rd ed), Venables and Ripley
Numerical Recipes in C (2nd ed), Press et al
Grading
Homework (30%), two midterms and final project (70%)
Exams
First midterm: February 20
Second midterm: April 7
Final project due on: May 12 at 11am
Students with disabilities: If you have special needs as addressed by the Americans
with Disabilities Act (ADA) and need assistance, please notify the Office of Disability
Services, A048 Brady Commons, 882-4696 or the course instructor immediately.
Reasonable efforts will be made to accommodate your special needs.
Honesty: Academic honesty is fundamental to the activities and principles of a
university. All members of the academic community must be confident that each
person’s work has been responsibly and honorably acquired, developed, and presented.
Any effort to gain an advantage not given to all students is dishonest whether or not the
effort is successful. The academic community regards academic dishonesty as an
extremely serious matter, with serious consequences that range from probation to
expulsion. When in doubt about plagiarism, paraphrasing, quoting, or collaboration,
consult the course instructor.
Syllabus
I.
1.
2.
3.
4.
II.
Numerical optimization
1. Standard methods
2. EM algorithm
3. Simulated annealing
III.
1.
2.
3.
4.
Approximate methods of inference
Laplace approximation
Gaussian quadrature
Importance sampling
Monte Carlo integration
1.
2.
3.
4.
Markov chain Monte Carlo
Gibbs sampling
Metropolis algorithm
Metropolis-Hastings algorithm
Convergence diagnostics
IV.
V.
Stochastic Simulation
Generation of discrete random quantities
Generation of continuous random quantities
Generation of random vectors and matrices
Acceptance/rejection methods
Further topics
1. Computation of marginal likelihood
2. Reversible jumps
3. Convergence acceleration