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
Sep 22, 2014
Fraydoun Rezakhanlou, UC Berkeley (Speaker)
Erdős-Rényi model is a random graph on n vertices where each edge is added independently with probability p. This may be regarded as a symmetric matrix with iid entries. In this lecture I discuss the large deviation principle of Sourav and Varadhan on the law of such matrices/graphs as the size gets large. One of the main tool is the work of Lovasz and Szegedy on “graphons”, that are the...
Large Deviation Principle for Erdős-Rényi random graphs
Laurent El Ghaoui, University of California, Berkeley (Speaker - Featured)
Wenpin Tang, Department of Statistics, U.C. Berkeley
In this talk, we deal with the problem of embedding some continuous-time processes of unit length into a Brownian path. Beginning with a discrete version of the problem, we derive the asymptotics of the expected waiting time for several interesting patterns. These suggest corresponding results on the existence or non-existence of continuous paths embedded in Brownian motion. With further effort,...
Debdeep Pati, Department of Statistics, Florida State University
Shrinkage priors are routinely used as alternative to point-mass mixture priors for sparse modeling in high-dimensional applications. The question of statistical optimality in such settings is under-studied in a Bayesian framework. We provide theoretical understanding of such Bayesian procedures in terms of two key phenomena: prior concentration around sparse vectors and posterior...
Bob Anderson, University of California, Berkeley (Speaker - Featured)