Statistics at UC Berkeley: We are a community engaged in research and education in probability and statistics. In addition to developing fundamental theory and methodology, we are actively involved in statistical problems that arise in such diverse fields as molecular biology, geophysics, astronomy, AIDS research, neurophysiology, sociology, political science, education, demography, and the U.S. Census. We have forged strong interdisciplinary links with other departments and areas of study, particularly biostatistics, mathematics, computer science, and biology, and actively seek to recruit graduate students and faculty who can help to build and maintain such links. We also offer a statistical consulting service each semester.
Statistics at UC Berkeley
Oct 8, 2014
Jose Menchero, Menchero Portfolio Analytics Consulting
Weak Concentration for First Passage Percolation Times on Graphs and General Increasing Set-valued Processes
David Aldous, Department of Statistics, U.C. Berkeley
A simple lemma bounds $s.d.(T)/\Ex T$ for hitting times $T$ in Markov chains with a certain strong monotonicity property. We show how this lemma may be applied to several increasing set-valued processes. Our main result concerns a model of first passage percolation on a finite graph, where the traversal times of edges are independent Exponentials with arbitrary rates. Consider the percolation...
Adityanand Guntuboyina, UC Berkeley
I will present a general technique for obtaining Bayes risk lower bounds for arbitrary priors in standard decision theoretic problems. The method leads to generalizations of a variety of classical minimax bounds. I will describe some applications to minimaxity and admissibility. This is based on joint work with Xi Chen and Yuchen Zhang.
Ryan McCorvie, University of California, Berkeley
Max Fathi, Department of Mathematics, U.C. Berkeley
In the 80s, De Giorgi introduced the notion of abstract gradient flows, which allowed to define a notion of solutions to ordinary differential equations of the form x ̇ = −∇F(x) on metric spaces (rather than Riemannian manifolds for the usual definition). In 2005, Ambrosio, Gigli and Savar ́e showed that when we consider the space of probability measures on...