Tools for understanding Markov chains as they arise in applications. Random walk on graphs, reversible Markov chains, Metropolis algorithm, Gibbs sampler, hybrid Monte Carlo, auxiliary variables, hit and run, Swedson-Wong algorithms, geometric theory, Poincare-Nash-Cheeger-Log-Sobolov inequalities. Comparison techniques, coupling, stationary times, Harris recurrence, central limit theorems, and large deviations. NOTE for both MATH and STATS: Undergraduates and Masters students who wish to enroll must fill out a Request for Review form: https://forms.gle/v5RojToYzmYxGvKc7 ; Your request will be reviewed by faculty and you'll be notified if you are granted permission to enroll.
3 units · Letter or Credit/No Credit
Tools for understanding Markov chains as they arise in applications. Random walk on graphs, reversible Markov chains, Metropolis algorithm, Gibbs sampler, hybrid Monte Carlo, auxiliary variables, hit and run, Swedson-Wong algorithms, geometric theory, Poincare-Nash-Cheeger-Log-Sobolov inequalities. Comparison techniques, coupling, stationary times, Harris recurrence, central limit theorems, and large deviations. NOTE for both MATH and STATS: Undergraduates and Masters students who wish to enroll must fill out a Request for Review form: https://forms.gle/v5RojToYzmYxGvKc7 ; Your request will be reviewed by faculty and you'll be notified if you are granted permission to enroll.
Offered in Spring 2026 at Stanford University.