PICAR: An Efficient Extendable Approach for Fitting Hierarchical Spatial Models

Hierarchical spatial models are very flexible and popular for a vast array of applications in areas such as ecology, social science, public health, and atmospheric science. It is common to carry out Bayesian inference for these models via Markov chain Monte Carlo (MCMC). Each iteration of the MCMC a...

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Veröffentlicht in:	Technometrics Jg. 64; H. 2; S. 187 - 198
Hauptverfasser:	Lee, Ben Seiyon, Haran, Murali
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Alexandria Taylor & Francis 03.04.2022 American Society for Quality
Schlagworte:	Algorithms Arrays Atmospheric models Basis functions Basis representation Bayesian analysis Cloud cover Computational efficiency Computing costs Cross correlation Fields (mathematics) Finite element method Gaussian random field Iterative methods Markov analysis Markov chain Monte Carlo Markov chains Monte Carlo simulation Non-Gaussian spatial data Ordinal spatial data Predictions Programming languages Public health Spatial data Spatially varying coefficients Statistical inference
ISSN:	0040-1706, 1537-2723
Online-Zugang:	Volltext
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