"Nearest-Neighbor Mixture Models for Non-Gaussian Spatial Processes" by Xiaotian Zheng, Athanasios Kottas et al.
We develop a class of nearest-neighbor mixture models that provide direct, computationally efficient, probabilistic modeling for non-Gaussian geospatial data. The class is defined over a directed acyclic graph, which implies conditional independence in representing a multivariate distribution through factorization into a product of univariate conditionals, and is extended to a full spatial process. We model each conditional as a mixture of spatially varying transition kernels, with locally adapt...
Mediterranean Sea Oceans General Bayesian Hierarchical Models Markov Chain Monte Carlo Spatial Statistics Ail Dependence
Source: uow.edu.au