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
Mar 25, 2019
Mar 19, 2019
Dec 13, 2018
Speakers: Farzad Pourbabaee, UC Berkeley (Speaker - Featured)
We consider the experimentation dynamics of a decision maker (DM) in a two-armed bandit setup, where the agent holds ambiguous beliefs regarding the distribution of the return process of one arm and is certain about the other one. The DM entertains Multiplier preferences a la Hansen and Sargent , thus we frame the decision making environment as a two-player differential game against nature...
Julia Palacios, Stanford University
In this talk I will present the Tajima coalescent, a model on the ancestral relationships of molecular samples. This model is then used as a prior model on unlabeled genealogies to infer evolutionary parameters with a Bayesian nonparametric method. I will then show that conditionally on observed data and a particular mutation model, the cardinality of the hidden state space of Tajima’s...
Speakers: David Sraer, UC Berkeley (Speaker - Featured)
Banks’ exposure to fluctuations in interest rates strongly forecasts excess Treasury bond returns. This result is consistent with optimal risk management decisions, a banking counterpart to the household Euler equation. In equilibrium, the bond risk premium compensates banks for bearing fluctuations in interest rates. When banks’ exposure to interest rate risk increases, the price of this risk...
Peter Song, University of Michigan
I will present a new statistical paradigm for the analysis of streaming data based on renewable estimation and incremental inference in the context of generalized linear models. Our proposed renewable estimation enables us to sequentially update the maximum likelihood estimation and inference with current data and summary statistics of historic data, but with no use of any historic raw data...
Speakers: Tingyue Gan, UC Berkeley (Speaker - Featured)