**Instructor:**
Professor Jeffrey S. Rosenthal,
Department of Statistics, University of Toronto

Sidney Smith Hall, room 6024; phone (416) 978-4594;
http://probability.ca/jeff/;

**Time:** Thursdays 12-2. First class Sept 11. Last class
Dec 4.

**Place:** Ramsey Wright Zoology Building, room 141. [New!]

**Textbook:**
None, but various references will be suggested.
(The
course notes
from a recent Lancaster course,
or the slides from a recent
introductory lecture,
may also be somewhat useful.)

**NEW!**
See the
STA4276 notes,
and some R commands (to
obtain R, visit CRAN).

**Content: **
We will describe such standard MCMC algorithms as the
Metropolis-Hastings algorithm and the Gibbs sampler. We will then
explore such issues as asymptotic convergence in distribution, geometric
ergodicity, quantitative convergence rate bounds, optimal scaling, and
perfect simulation algorithms. The issues will be discussed both in
terms of theoretical analysis, and in terms of simulation output.

**Prerequisites: **
STA 2111F or permission of the instructor.
Students should have a strong background in
real analysis and probability theory (including basic measure theory) and
stochastic processes (including Markov chains).

**Evaluation: **
See separate sheet.

See also my Markov chain Java applets.

This document is available at http://probability.ca/jeff/courses/sta4276-03a.html.