Supplementary files:notes.pdf, hw0.pdf, hw1.pdf, hw2.pdf, testsol.pdf, hw3.pdf, hw4.pdf, hw4clar.pdf, R files (directory), rwm.html (applet).

**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:** Tuesdays, 6-9 pm. First class September 11. Last
class December 4.

**Place:** Sidney Smith Hall (100 St. George Street), room 2110.
(Building "SS" on campus map.)

**Course Web Page:** Visit
probability.ca/sta410
for the latest course information and announcements.

**Textbook:** There is no
required textbook, but students are encouraged to consult the following
references. (The notes by Gray and by Jones
are freely available on-line, courtesy
of their authors; the other
books will be held on short-term loan at the Gerstein Science
Library, 7 King's College Circle.)

- G.H. Givens and J.A. Hoeting (2005), Computational Statistics. Wiley. [Gerstein / amazon]
- R. Gray (2001), Advanced Statistical Computing. Available on-line.
- G.L. Jones (2007), Computational Statistical Methods. Available on-line.
- J. F. Monahan (2001), Numerical Methods of Statistics. Cambridge University Press. [Gerstein / amazon]
- R. A. Thisted (1988), Elements of Statistical Computing. Chapman & Hall. [Gerstein / amazon]

**Tentative list of topics to be covered: **
Floating-Point Arithmetic, Numerical Optimisation, the EM Algorithm,
Numerical Integration, Pseudorandom Number Generation, Distributional
Sampling, Monte Carlo Methods, Markov Chain Monte Carlo (MCMC) algorithms,
Matrix Decomposition, Nonlinear Regression, the Bootstrap, Kernel Density
Estimation, Cross-Validation.

**Prerequisites: **
Knowledge of statistical methodology at the level of at least STA302,
and computer programming experience at the level of at least CSC108.

**Computing: **
Students will be required to write computer
programs using the "R" statistical software package.
Statistics Dept graduate students can access R on the
Statistics Dept
computers; undergraduate students can access R on
CQUEST. Alternatively, students
can install R on the computer(s) of their choice, by downloading its
"base" package (for free) from
probability.ca/cran
or www.r-project.org.
More information is available here.

**Evaluation:**
One in-class test (**Oct. 23**, 30%); one final exam (50%);
homework assignments (20%). Note: test will be in room 128 of the
**Mining Building** (170 College Street).
See also the grade-related course policies.

This document is available at http://probability.ca/jeff/teaching/0708/sta410/