PreprintCC BY 4.0HAL
Simulation-consistent Estimation of the Marginal Likelihood for Block Models
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.
Related
- Marginal maximum likelihood estimation and asymptotic theory for latent variable models in high dimensions
- Marginal Composite Likelihood Estimation of Bergomi Models from High-Frequency Option Prices
- Out-of-sample prediction in SEM: A simulation-based comparison of maximum likelihood and (consistent) partial least squares
- Neural composite likelihood estimation: simulation based inference for time series
- Estimating the marginal likelihood with Integrated nested Laplace approximation (INLA)
BibTeX
@misc{metodiev2026simulationconsistent,
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},
howpublished = {HAL},
year = {2026},
month = jul,
doi = {10.48550/arxiv.2607.23998},
eprint = {2607.23998},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05704613v2},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Simulation-consistent Estimation of the Marginal Likelihood for Block Models
AU - Martin Metodiev
AU - Marie Perrot-Dockès
AU - Guilhem Fouetillou
AU - Pierre Latouche
AU - Adrian E Raftery
PY - 2026
DA - 2026/07/27
PB - HAL
DO - 10.48550/arxiv.2607.23998
UR - https://hal.science/hal-05704613v2
AB - 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.
ER -