Title: On the equivalence of modes of convergence for log-concave distributions
Speaker: Elizabeth Meckes (Case Western Reserve University MAMS Department)
Abstract: There are many ways of quantifying convergence of probability distributions. I will begin the talk with a survey of notions of distance between probability measures and the relationships among them. I will then specialize to the case of log-concave distributions. We will see that many notions of distance which define different topologies in general are equivalent under the assumption of log-concavity. This is joint work with Mark Meckes.