[Ml-stat-talks] Michael Jordan seminar 3/28

Barbara Engelhardt bee at princeton.edu
Mon Mar 21 12:05:16 EDT 2016

Seminar of interest.

DATE:  Monday, March 28, 2016

TIME:  4:30pm

LOCATION:  Sherrerd Hall, room 101

SPEAKER:  Michael I. Jordan, University of California, Berkeley

TITLE:   Bayesian Nonparametrics and Combinatorial Stochastic Processes

ABSTRACT:   Bayesian nonparametric modeling and inference are based on
using general stochastic processes as prior distributions. Despite the
great generality of this definition, the great majority of the work in
Bayesian nonparametrics is based on only two stochastic processes: the
Gaussian process and the Dirichlet process. Motivated by the needs of
applications, I present a broader approach to Bayesian nonparametrics in
which priors are obtained from a class of combinatorial stochastic
processes known as "completely random measures" (Kingman, 1967). In
particular I will present models based on the beta process, the Bernoulli
process, the gamma process and the Dirichlet process, and on hierarchical
and nesting constructions that use these basic stochastic processes as
building blocks. I will discuss applications of these models to several
problem domains, including image processing, protein structural modeling,
natural language processing and statistical genetics.

Barbara E Engelhardt
Assistant Professor
Department of Computer Science
Center for Statistics and Machine Learning
Princeton University
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