Advanced Markov Chains

Graduate / Professional

In this course, we cover some advanced topics on the theory of Markov chains, with applications in algorithmic counting and sampling. The topics include more standard techniques, like canonical paths and the correlation decay algorithm, to very recent research topics such as spectral independence and stochastic localization. Prerequisites: undergraduate-level understanding of discrete mathematics, linear algebra, and (discrete) stochastic processes. Some background on real analysis and algorithms/computational complexity is beneficial. W 1:05pm-3:35pm