Abstract
Spatial covariance models have in general dense matrices. Therefore, estimation and prediction depend on approximations. This problem is a major concern in hierarchical models, such as the Poisson regression. We are going to study new approaches of approximate inference based upon Pólya-Gamma variables augmented likelihood via Hamiltonian Monte Carlo. We aim to provide concrete performanc…