摘要：Scale mixtures of normals have been discussed extensively in the literature as heavy-tailed alternatives to the normal distribution for robust modeling. They have been used either as error models to handle outliers or as prior distributions to provide more reasonable shrinkage of model parameters. The proposed method by Finegold and Drton goes beyond the existing literature both in terms of application (graphical models) and methodology (Dirichlet t) for outlier handling. While this approach can be applied to many other problems, in this discussion I will focus on its application in Bayesian modeling of high throughput biological data.
