Geometric Ergodicity of van Dyk and Meng's Algorithm for the Multivariate Student's t Model
多元 Student t 模型中 van Dyk–Meng 算法的几何遍历性
Let π denote the posterior distribution that results when a random sample of size n from a d-dimensional location-scale Student's t distribution (with ν degrees of freedom) is combined with the standard noninformative prior. van Dyk and Meng developed an efficient Markov chain Monte Carlo (MCMC) algorithm for sampling from π and provided considerable empirical evidence to suggest that their algorithm converges to stationarity much faster than the standard data augmentation…