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Unformatted text preview: STA 6934: Monte Carlo Statistical Methods Spring 2010 Professor George Casella Griffin-Floyd 225 [email protected] Credits: 3 Co-requisite: STA 6327 or permission of instructor. Teaching Assistant: Quan Tran Lecture and office hours Lectures: MWF Period 5: 11:45-12:35, Griffin- Floyd 230 Office Hours: To be announced Textbook Monte Carlo Statistical Methods, Second Edition , Robert and Casella. Course Web Page The web page for the course is http://www.stat.ufl.edu/ casella/MCSM10/ All important information will be posted there, including assignments and solutions. There will be no class (a) January 11,13,15 (b) January 18 (MLK Day) I will try to find a time to make up the missed classes. Course Notes Portions of this material have been taught as a short course over the years. There are 299 slides that I will be showing in class, but they will be supplemented, with much of the underlying theory done on the board. You can download the slides if you wish, and have them to take notes on. There are two versions available on the web page. One is formatted as slides, and in the other the slides are smaller with room to write. Homework 1 Homework There will be six homework assignments. Due dates are given on the schedule below. Everyone is expected to do every problem that is assigned. I would like to encourage you to hand in your assignments in La- tex, to get you more familiar with it. Latex is available as a free download from http://www.miktex.org/ or TeXshop for LaTeX (Mac) http://www.uoregon.edu/ koch/texshop/obtaining.html Each HW PROBLEM will be graded on a scale of 0-4 . Home- work is due in the TA mailbox by 5pm of the due date. If your home- work is late it will be graded on a scale of 1-3. Computing Throughout the course there will be examples and assignments requir- ing computing. I will typically illustrate things using R (available as a free download from http://www.r-project.org/). I will also use Win- Bugs, which is also free from http://www.mrc-bsu.cam.ac.uk/bugs/....
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- Fall '08
- Gibbs, Monte Carlo method, Markov chain Monte Carlo