spBayes for large univariate and multivariate point-referenced spatio-temporal data models
Abstract
In this paper we detail the reformulation and rewrite of core functions in the spBayes R package. These efforts have focused on improving computational efficiency, flexibility, and usability for point-referenced data models. Attention is given to algorithm and computing developments that result in improved sampler convergence rate and efficiency by reducing parameter space; decreased sampler run-time by avoiding expensive matrix computations, and; increased scalability to large datasets by implementing a class of predictive process models that attempt to overcome computational hurdles by representing spatial processes in terms of lower-dimensional realizations. Beyond these general computational improvements for existing model functions, we detail new functions for modeling data indexed in both space and time. These new functions implement a class of dynamic spatio-temporal models for settings where space is viewed as continuous and time is taken as discrete.
Cite
@article{arxiv.1310.8192,
title = {spBayes for large univariate and multivariate point-referenced spatio-temporal data models},
author = {Andrew O. Finley and Sudipto Banerjee and Alan E. Gelfand},
journal= {arXiv preprint arXiv:1310.8192},
year = {2013}
}