**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

Jan 17, 2020

Sep 25, 2019

Sep 9, 2019

Allan Sly, Princeton University

Abstract:

Replica Symmetry Breaking for Random Regular NAESAT
Ideas from physics have predicted a number of important properties of random constraint satisfaction problems such as the satisfiability threshold and the free energy (the exponential growth rate of the number of solutions). Another prediction is the condensation regime where most of the solutions are contained in a small number of clusters...

Moritz Voss, UCSB

Abstract:

ABSTRACT: We study the competition of two strategic agents for liquidity in
the benchmark portfolio tracking setup of Bank, Soner, Voss (2017),
both facing common aggregated temporary and permanent price impact
à la Almgren and Chriss (2001). The resulting stochastic linear quadratic
differential game with terminal state constraints allows for an
explicitly available open-loop Nash...

Alexander Volberg, Michigan State University

Abstract:

We improve the constant $\frac{\pi}{2}$ in $L^1$-Poincar\'e inequality on Hamming cube. For Gaussian space the sharp constant in $L^1$ inequality is known, and it is the square root of
$\frac{\pi}{2}$ (Maurey—Pisier). For Hamming cube the sharp constant is not known, and the square root of $\frac{\pi}{2}$ gives an estimate from below for this sharp constant. On the other hand, Ben Efraim and...

Tim Sullivan, Freie Universität Berlin and Zuse Institute Berlin

Abstract:

Numerical computation --- such as numerical solution of a PDE, or quadrature --- can modelled as a statistical inverse problem in its own right. In particular, we can apply the Bayesian approach to inversion, so that a posterior distribution is induced over the object of interest (e.g. the PDE's solution) by conditioning a prior distribution on the same finite information that would be used in a...

Neyman Seminar

Saad Mouti, UC Berkeley

Abstract:

ABSTRACT: