Departmental Colloquium
Wes Pegden
Carnegie-Mellon
Probability spaces driven by geometric constraints
Abstract: What can we understand about probability spaces on "nice" partitions of a
geometric region? Can we design efficient samplers? Can we at least detect
extreme outliers? These questions have become particularly salient in the
past several years as the techniques developed by mathematicians are now
applied to conduct statistical analyses of things like U.S. political
districtings. We will discuss some recent developments on probability
spaces defined by geometric constraints, including positive and negative
results on the mixing times of relevant Markov chains, Markov chain methods
which eschew mixing-time requirements, and direct sampling methods.
Friday February 20, 2026 at 3:00 PM in 636 SEO