A comparison between Markov approximations and other methods for large spatial data sets

DOI10.1016/j.csda.2012.11.011zbMath1349.62445arXiv1106.1980OpenAlexW2158359474WikidataQ57266351 ScholiaQ57266351MaRDI QIDQ333683

Finn Lindgren, David Bolin

Publication date: 31 October 2016

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1106.1980

zbMATH Keywords

waveletskrigingMarkov random fieldscovariance taperingMatérn covariancesprocess convolutions

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Random fields; image analysis (62M40)

Related Items (8)

Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy ⋮ Spatial Matérn Fields Driven by Non-Gaussian Noise ⋮ On the choice of the mesh for the analysis of geostatistical data using R-INLA ⋮ An ensemble solver for segregated cardiovascular FSI ⋮ Distributed Bayesian inference in massive spatial data ⋮ A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis ⋮ Sparse Approximations of Fractional Matérn Fields ⋮ Beyond the valley of the covariance function

Uses Software

• GMRFLib
• FRK

Cites Work

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