spbayes for large univariate and multivariate point-referenced spatio-temporal data models

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ID: 137836
2015
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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.
Reference Key
finley2015journalspbayes Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Andrew O. Finley;Sudipto Banerjee;Alan E. Gelfand
Journal open geospatial data, software and standards
Year 2015
DOI
10.18637/jss.v063.i13
URL
http://www.jstatsoft.org/index.php/jss/article/view/2228
Keywords
statistics

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