| Instructor: | Yuguo Chen |
| Lectures: | Mon/Wed: 2:05 - 3:20 p.m. 025 Old Chem |
| Office: | 216 Old Chemistry |
| Phone: | 681-8443 |
| email: | yuguo@stat.duke.edu |
| Office Hours: | by appointment |
| Textbook: | Recommended: Liu, Monte Carlo Strategies in Scientific Computing.
Optional: Gilks, Richardson and Spiegelhalter, Markov Chain Monte Carlo in Practice. Optional: Lange, Numerical Analysis for Statisticians . |
| Course Description: | An introduction to advanced statistical modeling and modern numerical methods useful in implementing statistical procedures for data analysis, model exploration, inference, and prediction. Methods are applied to substantial problems in discrete multivariate analysis, time series, econometrics, non-linear regression models, density estimation, applications with censored and missing data, hierarchical models, mixture modeling, and non-linear regressions. |
| Prerequisite: | Basic statistical theory, linear models, and Bayesian modeling at the level of STA214, STA215 and STA244. Some experience in a lower level programming language such as C or FORTRAN and a familiarity with UNIX workstations is assumed; experience with a high level language such as S-Plus or Matlab is useful, but not required. |
| Grades: | Course grades will be based on 1) weekly/biweekly assignments, 2) class presentation of a topic in computational inference or numerical statistical computing and 3) a final project. |