Sparse Interaction Neighborhood Selection for Markov Random Fields via Reversible Jump and Pseudoposteriors

N. Lopes Garcia, V. Freguglia

We consider the problem of estimating the interacting neighborhood of a Markov Random Field model with finite support and homogeneous pairwise interactions based on relative positions of a two-dimensional lattice. Using a Bayesian framework, we propose a Reversible Jump Monte Carlo Markov Chain algorithm that jumps across subsets of a maximal range neighborhood, allowing us to perform model selection based on a marginal pseudoposterior distribution of models.

Keywords: reversible jump MCMC; Pseudoposteriors

Functional Data Analysis, Spatial and Spatio-Temporal Statistics
September 2, 2026  5:40 PM
Aula 26

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