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
Giles Hooker, Cornell University
This talk examines the design of stochastic experimental systems so as to best able to estimate parameters of the underlying dynamics. In systems ranging from ecology, neurobiology and economics, models of system dynamics can be paired with laboratory experiments to estimate parameters and gain insight into their underlying dynamics. When this is done, several experimental parameters can be...
Allan Sly, Princeton University
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...
Tim Sullivan, Freie Universität Berlin and Zuse Institute Berlin
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...