Statistics at UC Berkeley

Shirshendu Ganguly, UC Berkeley
Jan 22, 2018 4:00pm 1011 Evans Hall
Abstract:
Statistical mechanics models are ubiquitous at the interface of probability theory, information theory, and inference problems in high dimensions. In this talk, we will focus on sparse networks, and polymer models on lattices. The study of rare behavior (large deviations) is intimately related to the understanding of such models. In particular, we will consider the rare events that a sparse...
Shirshendu Ganguly, UC Berkeley
Jan 22, 2018 4:10pm 1011 Evans Hall
Abstract:
Statistical mechanics models are ubiquitous at the interface of probability theory, information theory, and inference problems in high dimensions. In this talk, we will focus on sparse networks, and polymer models on lattices. The study of rare behavior (large deviations) is intimately related to the understanding of such models. In particular, we will consider the rare events that a sparse...
Speaker: Mariana Olvera-Cravioto, UC Berkeley (Speaker - Featured)
Jan 25, 2018 12:30pm 1011 Evans Hall
Abstract:
The talk will center around a set of recent results on the analysis of Google’s PageRank algorithm on directed complex networks. In particular, it  will focus on the so-called power-law hypothesis, which states that the distribution of the ranks produced by PageRank on a scale-free graph (whose in-degree distribution follows a power-law) also follows a power-law with the same tail-index as the...
Merle Behr, University of Göttingen
Jan 31, 2018 4:00pm 1011 Evans Hall
Abstract:
A challenging problem in cancer genetics is that tumors often consist of a few different groups of cells, so called clones, where each clone has different mutations, like copy-number (CN) variations. In whole genome sequencing the mutations of the different clones get mixed up, according to their relative unknown proportion in the tumor. However, CN's of single clones can only take values in a...
Speaker: Markus Pelger, Stanford (Speaker - Featured)
Feb 1, 2018 12:30pm 1011 Evans Hall
Abstract:
This papers deals with the approximation of latent statistical factors with sparse and easy-to-interpret proximate factors. Latent factors in a large-dimensional factor model can be estimated by principal component analysis, but are usually hard to interpret. By shrinking the factor weights, we obtain proximate factors that are easier to interpret. We show that proximate factors consisting of...

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.