This course will discuss in depth advanced statistical computing methods used in scientific, especially biomedical, applications: generation of random numbers, optimization methods, numerical integration and advanced computational tools such as the EM algorithm, importance sampling, Gibbs sampler, Metropolis Hastings, auxiliary variable methods, data augmentation, reversible jump MCMC, and population-based Monte Carlo. The second half of the course will involve detailed discussions of statistical models and methods for problems in genomics and computational biology, including dynamic programming, hidden Markov models, multiple sequence alignment, phylogenetic tree reconstruction, gene regulatory network discovery and analysis of genome tiling array data. Computer programming exercises would apply the methods discussed in class, primarily using the software R and BUGS/WinBUGS. During the course, students will form small groups to select a topic of interest, on which they will carry out a course project implementing statistical computing methods appropriate for the application.

Note that this information may change at any time. Please visit the Student Link for the most up-to-date course information.