PreprintarXiv licencearXiv
Efficient Bayesian inference for multiple network data
Abstract
We investigate distributional properties of the centered Erdős--Rényi distribution (Lunagòmez et al., 2021) and propose a semi-conjugate Bayesian approach to multiple network data. In simulations, both Gibbs sampling and empirical Bayes accurately recover network summaries, with the latter scaling efficiently with network size. As a companion to this note, we provide the R package BayesCER, which implements the proposed methodology.
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BibTeX
@misc{barile2026efficient,
title = {{Efficient Bayesian inference for multiple network data}},
author = {Francesco Barile and Sara Capozio and Bernardo Nipoti},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.01532},
eprint = {2610.01532},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.01532},
}
RIS
TY - UNPB
TI - Efficient Bayesian inference for multiple network data
AU - Francesco Barile
AU - Sara Capozio
AU - Bernardo Nipoti
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.01532
UR - https://arxiv.org/abs/2610.01532
AB - We investigate distributional properties of the centered Erdős--Rényi distribution (Lunagòmez et al., 2021) and propose a semi-conjugate Bayesian approach to multiple network data. In simulations, both Gibbs sampling and empirical Bayes accurately recover network summaries, with the latter scaling efficiently with network size. As a companion to this note, we provide the R package BayesCER, which implements the proposed methodology.
ER -