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Activity Number: 340 - SPEED: Bayesian Methods, Part 1
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #304618
Title: Variational Inference for Latent Space Models for Dynamic Networks
Author(s): Yan Liu* and Yuguo Chen
Companies: University of Illinois at Urbana-Champaign and University of Illinois at Urbana-Champaign
Keywords: Dynamic network; Latent space model; Variational inference ; Bayes risk

Latent space models are popular for analyzing dynamic network data. We propose a variational approach to estimate the model parameters as well as the latent positions of the nodes in the network. The variational approach is much faster than Markov chain Monte Carlo algorithms, and is able to handle large networks. Theoretical properties of the variational Bayes risk of the proposed procedure are provided. We apply the variational method and latent space model to simulated data as well as real data sets to demonstrate its performance.

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

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