Counting and Sampling

3 credits · Undergraduate

This course will cover various algorithm design techniques for two intimately connected class of problems: sampling from complex probability distributions and counting combinatorial structures. A large part of the course will cover Markov Chain Monte Carlo techniques: coupling, stationary times, canonical paths, Poincare and log-Sobolev inequalities. Other topics include correlation decay in spin systems, variational techniques, holographic algorithms, and polynomial interpolation-based counting. Prerequisites: CS161 or equivalent, STAT116 or equivalent.