Prefer a chat interface with context about you and your work?
Computational Aspects Related to Inference in Gaussian Graphical Models With the G-Wishart Prior
We describe a comprehensive framework for performing Bayesian inference for Gaussian graphical models based on the G-Wishart prior with a special focus on efficiently including nondecomposable graphs in the model space. We develop a new approximation method to the normalizing constant of a G-Wishart distribution based on the Laplace approximation. …