2013/10/30 by Andrew O. Finley, Sudipto Banerjee, Finley, Andrew O. +3 · 2 citations
Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #Methodology (stat.ME) #stat.CO #stat.ME
paper · pdf · doi:10.48550/arxiv.1310.8192
arxiv created 2013/10/30 · arxiv updated 2013/10/31
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.