| Instructor | Roger Peng |
| Meeting Time | Tues, Thurs, 8:30-9:50am |
| Room | W4007 |
| Office hours | Please just send me an email (rpeng AT jhsph.edu) or drop by my office (E3535) |
Overview
This course covers the theory and application of common algorithms used in statistical computing. Topics include root finding algorithms, optimization algorithms, numerical integration methods, Monte Carlo, Markov chain Monte Carlo, stochastic optimization, and bootstrapping. In addition we will cover computing topics relevant to the implementation of these statistical algorithms, including computer organization, memory design, floating point representation, operating system design, and parallel computing.
Textbooks and Reading
The textbooks for the class are:- Ken Lange, Numerical Analysis for Statisticians. We will not cover every topic in the book but most of the important ones.
- Andrew S. Tanenbaum, Structured Computer Organization
- Gilks, Richardson, and Spiegelhalter, Markov Chain Monte Carlo in Practice
The textbooks will be supplemented with handouts given in class.
Homework
Students are required to use LaTeX for typesetting and the R programming language for computing. There may be some assignments requiring the use of the C programming language.
There will be roughly one homework assignment per 2 weeks. The homeworks will typically be a mix of reading, programming, and mathematical exercises.
Grading and Exams
Grading will be based on bi-weekly homeworks, an in-class final exam based on the readings, and in-class participation in discussions of relevant journal articles.
Prerequisites
Students should have completed at least one year of doctoral-level statistics/biostatistics theory and methods courses and should be familiar with the R programming language.