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. To show the strength of our proposed methodology we perform a simulation study and apply it to a real dataset from a discrete texture image analysis.

翻译：我们考虑过对Markov随机场模型互动邻里进行估算的问题,该模型有有限的支持和基于二维阵列相对位置的同质对齐互动。我们用一个贝叶斯框架,提出一个翻转式跳跃跳跃蒙泰·卡洛·马尔科夫连锁算法,跳过最大范围邻里子集,使我们能够根据模型的边际假面分布进行模型选择。为了显示我们拟议方法的强度,我们进行了模拟研究,并将其应用到一个来自离散纹理图像分析的真实数据集中。