AMS 207 - Spring 2005

GENERAL COURSE DESCRIPTION This course is a continuation of AMS-206. The course will have an emphasis on statistical modeling from a Bayesian perspective. Some of the topics that will be covered include: hierarchical modeling, linear models (regression and analysis of variance), multivariate regression models and mixture models. Computational simulation-based methods such as MCMC will be studied and used for parameter estimation and prediction. The prerequisite for this class is AMS-206 (former ENGR-206). If you are taking this class you should be familiar with R and/or with any other programming language (C, C++, F77, F95, Matlab or similar) at a level that allows you to write relatively complex code to fit models with multiple parameters. The course webpage is www.soe.ucsc.edu/classes/ams207/Spring05/ You can find a detailed schedule here (please check this site at least once per week as it will be constantly updated!)  TEXTBOOK    The required textbook is: Bayesian Data Analysis, Second Edition. A. Gelman, J.B. Carlin, H.S. Stern and D.B. Rubin. Chapman and Hall/CRC.  Here is a list of other recommended  books (not required): Markov Chain Monte Carlo, Dani Gamerman, Chapman and Hall/CRC Bayesian Statistical Modelling, Peter Congdon, Wiley Applied Bayesian Modelling, Peter Congdon, Wiley HOMEWORK, QUIZZES, PROJECT AND EXAM The course work will be weighted as follows: Quizzes (40%), Final (35%) and Project (25%). There will be two quizzes (40% total, 20% each, check the dates online) and one final (35% on 06/07/05, 4-7pm).  Usually, the quizzes and the final will have two parts: one to be taken in class and another one to take home. The take home part will involve the analysis of a case study and/or the application of the methodology covered in class. There will be one project (25%). The project will involve two short (max. 10 min) presentations (10% of the grade) in class during weeks 6 and 10 and a final report (max. 4 pages) due on Friday 06/03 at 4pm (15% of the grade). There will be regular homework assignments. The homework will not be collected and graded, however, it is extremely important that you work on the assignments for two reasons: (1) solving the homework problems is key to learn and understand the course material and (2) the problems in the homework will give a very close indication of the material that will be covered in exams and quizzes. Some of the homework problems will involve numerical exercises.

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