Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets.

ABSTRACT: Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations becomes large. There is a burgeoning literature on approaches for analyzing large spatial datasets. In this article, we propose a divide-and-conquer strategy within the Bayesian paradigm. We partition the data into subsets, analyze each subset using a Bayesian spatial process model and then obtain approximate posterior inference for the entire dataset by combining the individual posterior distributions from each subset. Importantly, as often desired in spatial analysis, we offer full posterior predictive inference at arbitrary locations for the outcome as well as the residual spatial surface after accounting for spatially oriented predictors. We call this

SUBMITTER: Guhaniyogi R 

PROVIDER: S-EPMC6474670 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature