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Activity Number: 455 - Recent Advances in Multiple Graph Inference
Type: Invited
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #327025 Presentation
Title: Scalable Bayes Inference on Big Dependent Networks
Author(s): David B Dunson*
Companies: Duke University

Increasingly in a wide variety of application areas, ranging from neuroscience to the tech industry, data are collected on multiple dependent network-structured data, with enormous numbers of nodes in each network. Most approaches for statistical analysis of huge network data focus on a single network, and relatively simple statistical model and/or analysis task. For example, the entire focus may be on community detection. In this talk, we describe general divide-and-conquer MCMC algorithms for fitting broad classes of Bayesian hierarchical models to network data. The proposed algorithms are based on carefully designed subsample-based likelihood approximations, which enable MCMC to be conducted in an embarassingly parallel manner for different chunks of the network, with the results then combined in a simple communication efficient manner. The proposed likelihood approximations are related to stratified sampling approaches in the survey sampling and epidemiology literature. The methods are applied to data on brain connectomes.

Authors who are presenting talks have a * after their name.

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