Simulation-consistent Estimation of the Marginal Likelihood for Block Models
Methodology
2026-07-27 v1
Abstract
We propose a methodology for computing marginal likelihoods for block models. The proposed estimator computes the marginal likelihood from Markov chain Monte Carlo (MCMC) samples and is simulation-consistent, even when the size of the dataset is fixed. Moreover, it is asymptotically normal, of finite variance, invariant to label switching and can be computed efficiently, even for models with an arbitrarily large number of components. We evaluate the method through simulation studies in settings where the true marginal likelihood is available analytically. Finally, we apply the approach to a social network dataset based on the 2023 United Nations Climate Change Conference (COP28) and discuss the resulting insights.
Cite
@article{arxiv.2607.23998,
title = {Simulation-consistent Estimation of the Marginal Likelihood for Block Models},
author = {Martin Metodiev and Marie Perrot-Dockès and Guilhem Fouetillou and Pierre Latouche and Adrian E. Raftery},
journal= {arXiv preprint arXiv:2607.23998},
year = {2026}
}