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Spatial and Spatio-temporal Bayesian Models with R - INLA By Marta Blangiardo, Michela Cameletti
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- Sales Rank: #988365 in eBooks
- Published on: 2015-04-07
- Released on: 2015-04-07
- Format: Kindle eBook
From the Back 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.
About the Author
Marta Blangiardo, MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, UK
Michela Cameletti, Department of Management, Economics and Quantitative Methods, University of Bergamo, Italy
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