---------- Forwarded message --------- From: Daniel Lewis Sussman<[hidden email]> Date: Wed, Jan 22, 2020, 11:25 PM Subject: Fwd: Seminar Announcement- Statistical Neuroscience Seminar To: <[hidden email]>, <[hidden email]>
In case you haven't already received this, please see below for the announcement for this week's Probability and Statistics seminar.
---------- Forwarded message ---------
The Department of Mathematics & Statistics are happy to present the Statistical Neuroscience Seminar with our guest speaker Keith Levin on January 23rd, 4pm in MCS B39. Please see below for
the full seminar announcement.
Statistical Neuroscience Seminar
Bootstrapping Networks with Latent Geometric Structure
January 23rd, 4pm MCS B39
Tea and Cookies at 3:30pm
A core problem in statistical network analysis is to develop network analogues of classical statistical techniques. The problem of bootstrapping network data stands
out as especially challenging, owing to the dependency structure of network data and the fact that one typically observes only a single network, rather than a sample. In this talk, I will present a method for generating bootstrap samples for networks drawn
from latent space models, a class of network models in which unobserved geometric structure drives network topology. We show consistency of the proposed bootstrap method under the random dot product graph, a latent space model that includes the popular stochastic
blockmodel as a special case, though the method is applicable to any latent space model in which the latent geometry can be recovered suitably accurately. In the second half of the talk, I will outline a few ongoing projects applying this bootstrap method
and several related network analysis techniques to neuroscientific data obtained from fMRI studies. Common to these projects is the presence of latent low-dimensional network structure that we wish to relate to patient-level covariates such as age or disease