Bayesian Inference on a Mixture Model With Spatial Dependence
提出一种基于拉普拉斯近似的新技术，用于选择具有空间依赖性的混合模型中的成分数量，并解决了隐藏Potts模型归一化常数难处理的问题，应用于卫星图像分析。
We introduce a new technique to select the number of components of a mixture model with spatial dependence. The method consists of an estimation of the integrated completed likelihood based on a Laplace’s approximation and a new technique to deal with the normalizing constant intractability of the hidden Potts model. Our proposal is applied to a real satellite image. Supplementary materials are available online.