This 4-unit course covers recent developments in the Markov chain Monte Carlo (MCMC) literature with an emphasis on the challenges associated with both big data and high-dimensional statistical models. The course starts with a quick review of the theoretical foundations of MCMC, Metropolis-Hastings and the Gibbs sampler. Next, we cover Hamiltonian Monte Carlo. In addition to learning advanced MCMC algorithms, students will also learn to use prominent MCMC software such as Nimble and Stan.
This course iterates between formal lectures and coding assignments. Students will develop their own MCMC code throughout, and the final project involves the rigorous implementation of one or more of the methods encountered during the quarter.
Tues/Thur, 1-230pm @ CHS 61235
Andrew J. Holbrook
Email: aholbroo@g.ucla.edu
Office hours: offered generously; please email me.
Special thanks to Dr. Marc Suchard and Dr. Xiang Ji for their invaluable help.