论文标题
马尔可夫随机场的稀疏互动邻居选择通过可逆跳跃和伪托带
Sparse Interaction Neighborhood Selection for Markov Random Fields via Reversible Jump and Pseudoposteriors
论文作者
论文摘要
我们考虑了基于二维晶格的相对位置，估计马尔可夫随机场模型的相互作用邻居的问题。使用贝叶斯框架，我们提出了一种可逆的跳跃蒙特卡洛马尔可夫链算法，该算法跳过了最大范围邻域的子集，使我们能够基于模型的边缘伪型分布进行模型选择。为了显示我们提出的方法的强度，我们进行了仿真研究，并将其应用于离散纹理图像分析中的真实数据集。
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.
