Nonparametric Bayes dynamic modelling of relational data

Journal Article (Journal Article)

Symmetric binary matrices representing relations are collected in many areas. Our focus is on dynamically evolving binary relational matrices, with interest being on inference on the relationship structure and prediction. We propose a nonparametric Bayesian dynamic model, which reduces dimensionality in characterizing the binary matrix through a lower-dimensional latent space representation, with the latent coordinates evolving in continuous time via Gaussian processes. By using a logistic mapping function from the link probability matrix space to the latent relational space, we obtain a flexible and computationally tractable formulation. Employing á¹”olya-gamma data augmentation, an efficient Gibbs sampler is developed for posterior computation, with the dimension of the latent space automatically inferred. We provide theoretical results on flexibility of the model, and illustrate its performance via simulation experiments.We also consider an application to co-movements in world financial markets.

Full Text

Duke Authors

Cited Authors

  • Durante, D; Dunson, DB

Published Date

  • December 1, 2014

Published In

Volume / Issue

  • 101 / 4

Start / End Page

  • 883 - 898

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asu040

Citation Source

  • Scopus