|Authors:||Andrew O. Finley, Sudipto Banerjee, Alan E. Gelfand|
|Title:||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.|
Page views:: 4279. Submitted: 2013-06-03. Published: 2015-02-13.
spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.