Open Access
2021 Truncated simulation and inference in edge-exchangeable networks
Xinglong Li, Trevor Campbell
Author Affiliations +
Electron. J. Statist. 15(2): 5117-5157 (2021). DOI: 10.1214/21-EJS1916


Edge-exchangeable probabilistic network models generate edges as an i.i.d. sequence from a discrete measure, providing a simple means for statistical inference of latent network properties. The measure is often constructed using the self-product of a realization from a Bayesian nonparametric (BNP) discrete prior; but unlike in standard BNP models, the self-product measure prior is not conjugate the likelihood, hindering the development of exact simulation and inference algorithms. Approximation via finite truncation of the discrete measure is a straightforward alternative, but incurs an unknown approximation error. In this paper, we develop methods for forward simulation and posterior inference in random self-product-measure models based on truncation, and provide theoretical guarantees on the quality of the results as a function of the truncation level. The techniques we present are general and extend to the broader class of discrete Bayesian nonparametric models.

Funding Statement

This work is supported by a National Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant and Discovery Launch Supplement.


Download Citation

Xinglong Li. Trevor Campbell. "Truncated simulation and inference in edge-exchangeable networks." Electron. J. Statist. 15 (2) 5117 - 5157, 2021.


Received: 1 May 2020; Published: 2021
First available in Project Euclid: 8 December 2021

Digital Object Identifier: 10.1214/21-EJS1916

Keywords: Bayesian inference , Bayesian nonparametrics , edge-exchangeable , networks , truncation

Vol.15 • No. 2 • 2021
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