Prerequisites
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.

Course Description (from the Duke Statistics course listings)
Techniques for maximization and integration suitable for computations in statistical analyses and particularly Bayesian inference. Quadrature methods; simulated annealing; Metropolis techniques; methods of Monte-Carlo and Gibbs sampling; error analysis, variance reduction, and comparisons with standard derivative-based optimization methods and quadrature-based integration methods.

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.

Grading
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) an end-of-semester project. Computing exercises will involve programming in R or S-Plus with occasional exercises involving simple C (or Fortran--your choice) programming.