Advanced spatial modeling with stochastic partial differential equations using R and INLA / Elias T. Krainski, Virgilio Gómez-Rubio, Haakon Bakka, Amanda Lenzi, Daniela Castro-Camilo, Daniel Simpson, Finn Lindgren, Håvard Rue.
Contributor(s): Gómez-Rubio, Virgilio | Bakka, Haakon | Lenzi, Amanda | Castro-Camilo, Daniela | Simpson, Daniel | Lindgren, Finn | Rue, Håvard.Material type: BookPublisher: Boca Raton : CRC Press, Taylor & Francis Group, 2019.Description: xiii, 283 pages : illustrations (some colour) ; 25 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781138369856; 1138369853.Other title: Advanced spatial modeling with stochastic partial differential equations using R and integrated nested Laplace approximation.Subject(s): STOCHASTIC DIFFERENTIAL EQUATIONS | MATHEMATICAL MODELS | RANDOM PROCESSES | LAPLACE TRANSFORMATIONS | RHoldings: GRETA POINT: 519.22 KRA
|Item type||Current location||Call number||Copy number||Status||Date due||Barcode|
|BOOK||WELLINGTON BOOKS||519.22 KRA||1||Issued||25/06/2019||B019589|
A Chapman & Hall Book.
Includes bibliographical references and index.
What this book is and isn’t -- 1. The Integrated Nested Laplace Approximation and the R-INLA package -- 2. Introduction to spatial modeling -- 3. More than one likelihood -- 4. Point processes and preferential sampling -- 5. Spatial non-stationarity -- 6. Risk assessment using non-standard likelihoods -- 7. Space-time models -- 8. Space-time applications -- List of symbols and notation -- Packages used in the book -- Bibliography -- Index.
Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications.
GRETA POINT: 519.22 KRA