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Spatial and Spatio-Temporal Bayesian Models With R - Inla Hardback - 2015
by Blangiardo, Marta,
- New
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Details
- Title Spatial and Spatio-Temporal Bayesian Models With R - Inla
- Author Blangiardo, Marta,
- Binding Hardback
- Edition Hardback
- Condition New
- Pages 320
- Volumes 1
- Language ENG
- Publisher Wiley
- Publication date 2015-06-02
- Illustrated Yes
- Features Bibliography, Illustrated, Index
- Bookseller's Inventory # 15574246-n
- ISBN 9781118326558 / 1118326555
- Weight 1.15 lbs (0.52 kg)
- Dimensions 9 x 6 x 0.8 in (22.86 x 15.24 x 2.03 cm)
-
Themes
- Aspects (Academic): Reference
- Category Mathematics
- Library of Congress subjects Bayesian statistical decision theory, Spatial analysis (Statistics)
- Library of Congress Catalogue Number 2015000696
- Dewey Decimal Code 519.542
- Quantity available 5
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From the publisher
From the rear cover
The reference book for spatio-temporal modeling with INLA
The Bayesian approach is particularly effective at modeling large datasets including spatial and temporal information due to its flexibility and ease with which it can formally include correlation and hierarchical structures in the data. However, classical simulation methods such as Markov Chain Monte Carlo can become computationally unfeasible; this book presents the Integrated Nested Laplace Approximations (INLA) approach as a computationally effective and extremely powerful alternative.
Spatial and Spatio-temporal Bayesian Models with R-INLA introduces the basic paradigms of the Bayesian approach and describes the associated computational issues. Detailing the theory behind the INLA approach and the R-INLA package, it focuses on spatial and spatio-temporal modeling for area and point-referenced data.
The combination of detailed theory and practical data analysis is beneficial for readers at any level. The coding of all the examples in R-INLA and the availability of all the datasets used throughout the book on the INLA website (www.r-inla.org) make an appealing feature for applied researchers wanting to approach or increase their knowledge and practice of the INLA method.
