Course covers numerical optimization, Markov Chain Monte Carlo (MCMC), estimation-maximization (EM) algorithms, Gaussian processes, Hamiltonian Monte Carlo, statistical/machine learning, data augmentation algorithms, and techniques for dealing with missing data. Students will also become proficient with the Python programming language, and its use for statistical computing.
Bios 6341 (Fundamentals of Probability), Bios 6342 (Contemporary Statistical Inference), or permission of instructor. Students must be familiar with the Git version control system and be prepared to program in Python.
Chris Fonnesbeck, PhD, Assistant Professor of Biostatistics
11137, 11th floor, 2525 West End Avenue
Nick Strayer, PhD Candidate
Clients for most computing and mobile platforms can be downloaded from the Slack website, or students may use the web client via a desktop browser.