GSO Spring Speaker 2016

Probabilistic Modeling of Big Table and Networks

David B. Dunson
Arts and Sciences Distinguished Professor
Departments of Statistical Science, Mathematics, and Electrical & Computer Engineering, Duke University

Start Date and Time: Fri, 22 Apr 2016, 10:30 AM

End Date and Time: Fri, 22 Apr 2016, 11:20 AM

Venue: BRNG 2290


In applications, data consist of high-dimensional complex and highly-structured discrete data. Our focus here is on high-dimensional unordered categorical data, which arise in epidemiology, social surveys and brain connectomics. In the first part of the talk, I will focus on data that can be structured as a multiway contingency table but otherwise have no obvious structure a priori. For such problems, we rely on probabilistic tensor factorizations, introducing new classes of factorizations, discussing relationships with sparse log-linear models, sketching theory on rates of convergence, and considering applications in social science surveys and genomics. In the second part of the talk, I focus on the case in which the categorical data consist of indicators of connections between pairs of nodes in a network, motivated in particular by brain connectomic studies. The probability distribution for such network-valued random variables can be conveniently represented via a hierarchical latent space representation. We propose a Bayesian approach to inference and show exciting results in performing inferences on differences in brain structure with phenotypes.

Purdue Department of Statistics, 250 N. University St, West Lafayette, IN 47907

Phone: (765) 494-6030, Fax: (765) 494-0558

© 2018 Purdue University | An equal access/equal opportunity university | Copyright Complaints

Trouble with this page? Disability-related accessibility issue? Please contact the College of Science Webmaster.