Exact Bayesian Inference for Multivariate Spatial Data of Any Size with Air Pollution Application

Music City Center 

Fine particulate matter and aerosol optical thickness are of interest to atmospheric scientists for understanding air quality and its various health/environmental impacts. The available data are extremely large, making uncertainty quantification in a fully Bayesian framework quite difficult, as traditional implementations do not scale reasonably to the size of the data. We specifically consider roughly 8 million observations from a remote sensing dataset obtained from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. To analyze data on this scale, we introduce Scalable Multivariate Exact Posterior Regression (SM-EPR) which combines the data subset approach and Exact Posterior Regression (EPR). EPR is a Bayesian hierarchical model that allows sampling of fixed and random effects directly from the posterior without Markov chain Monte Carlo (MCMC) or approximate Bayesian techniques. We extend EPR to the multivariate spatial context, where the multiple variables may be distributed according to different distributions. The combination of the data subset approach with EPR allows one to perform exact Bayesian inference without MCMC for effectively any sample size.

Basis functions

Big Data

Multivariate

Uncertainty quantification 

Section on Statistics and the Environment