Spatial prediction and mapping temperature - Serra, Laura;Juan, Pablo;Varga, Diego
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Temperature, one of the most important atmospheric variables, has a direct impact on physical and biological processes and its analysis in space and time play a crucial role in studying climate change. Here the results of a comparison between two ways of estimating models of spatial dependence are evaluated: kriging methods and Bayesian inference using the Integrated Nested Laplace Approximation (INLA).

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Produktbeschreibung
Temperature, one of the most important atmospheric variables, has a direct impact on physical and biological processes and its analysis in space and time play a crucial role in studying climate change. Here the results of a comparison between two ways of estimating models of spatial dependence are evaluated: kriging methods and Bayesian inference using the Integrated Nested Laplace Approximation (INLA).
Autorenporträt
Laura Serra Saurina degree in Mathematics and Master in Mathematics for Financial Instruments from the Universitat Autònoma de Barcelona (UAB) and holds a PhD in Statistics from the University of Girona (UdG). She is currently a researcher at the Center for Health Research (CISAL) and professor of epidemiology and biostatistics methods at the UPF.