5 papers
Simulation-consistent Estimation of the Marginal Likelihood for Block Models
Martin Metodiev, Marie Perrot-Dockès, Guilhem Fouetillou +2
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 a…
Efficiently Computed Marginal Likelihoods for Multivariate Mixture Models
Martin Metodiev, Nicholas J. Irons, Marie Perrot-Dockès +2
We propose a new methodology for efficiently computing marginal likelihoods for univariate and multivariate mixture models. The estimator that this methodology proposes computes th…
The Principle of Redundant Reflection
Martin Metodiev, Maarten Marsman, Lourens Waldorp +2
The fact that redundant information does not update a rational belief implies that rational beliefs are updated using Bayes rule. In the framework of Hild (1998a), this is true und…
A Structured Estimator for large Covariance Matrices in the Presence of Pairwise and Spatial Covariates
Martin Metodiev, Marie Perrot-Dockès, Sarah Ouadah +4
We consider the problem of estimating a high-dimensional covariance matrix from a small number of observations when covariates on pairs of variables are available and the variables…
Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator
Martin Metodiev, Marie Perrot-Dockès, Sarah Ouadah +2
We propose an easily computed estimator of marginal likelihoods from posterior simulation output, via reciprocal importance sampling, combining earlier proposals of DiCiccio et al…