Bayesian Nonparametric Modeling of Network Data

Daniele Durante - Universita Degli Studi Di Padova
Friday, February 17, 2017 -
3:30pm to 4:30pm
durante@stat.unipd.it
Old Chemistry 116

Abstract: 

Many fields of research provide increasingly complex data along with novel motivating applications and new methodological questions. In approaching these data sets it is fundamental to rely on parsimonious representations which make the problem tractable and provide interpretable inference procedures to draw meaningful conclusions. However, in reducing complexity, it is important to avoid restrictive models that lead to inadequate characterization of relevant patterns underlying the observed data. Within this framework, network data representing relationship structures among a set of nodes are a relevant example. Although there has been abundant focus on models for a single network, there is a lack of methods for replicated network-valued data monitored in different times or collected from a common population distribution. These data open new avenues for studying underlying connectivity patterns, how they are distributed in the population and if this distribution changes in time or across predictors of interest. Motivated by neuroscience and social science applications, I will discuss some issues associated with available statistical models and I will outline recent methods I proposed to cover some of the current gaps via Bayesian nonparametric models leveraging latent space representations.



Series: 
Statistical Science Seminar Series

Description: 

Seminars generally take place in 116 Old Chem Building on Fridays from 3:30 - 4:30 pm. However, please check individual abstracts to confirm time and location. Refreshments will be served after the seminars in Old Chemistry 211. Metered Parking is available at various locations on campus. If you have never visited us before, please see our driving directions and map. The easiest and most convenient parking areas are located at the Bryan Center parking garage near Duke Statistics (recommended) or at the Sarah P. Duke Gardens. Please email or call Karen Whitesell for additional information: karen.whitesell@duke.edu or phone 919-684-8029. Sorry, but we do not have reprints available. Please feel free to contact the authors by email for follow-up information, articles, etc. Reception following seminar in 211 Old Chemistry