Spatial and Spatio–temporal Bayesian Models with R – INLA (Anglais) Relié – 12 mai 2015
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Description du produit
Présentation de l'éditeur
Quatrième de couverture
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.
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Commentaires client les plus utiles sur Amazon.com
Congratulations to both authors !
Loma Libda University
Approximation (INLA) is a deterministic algorithm that avoids much of the computational burden that simulated methods such as Markov Chain Monte Carlo (MCMC) methods use.
Since I have almost exclusively worked with the frequentist approach, I appreciated that this book went into detail describing the methods and interpretation behind Bayesian statistics. The book does a nice job of describing the theory, but then balancing the theory with simple examples using R code that walks you step by step through the various algorithms in the book. The INLA algorithms seem to be implemented in lower-level languages for efficiency purposes (I am guessing C), but the book does an outstanding job of stepping outside of the complexity and creating step by step R code that describes the various algorithms. I wish more texts employed this teaching technique!
While the R-INLA website has very good examples and tutorials on their website, I did not feel that I fully understood the INLA method until reading this book. For those of us that are years removed from academia, it can be hard to "jump" into a formal statistical journal article and
understand the technique. This book has allowed me to know the method well enough in order to spread the technique to others in my organization. Again, thanks to the authors of the text as well as the excellent work by the INLA team.
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