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Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations
- Sales Rank: #475794 in Books
- Published on: 2015-06-02
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .80" w x 6.20" l, .92 pounds
- Binding: Hardcover
- 320 pages
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
Most helpful customer reviews
6 of 6 people found the following review helpful.
This text probably turned me Bayesian
By Mike Gahan
I had pre-ordered this text back in the Fall of 2014 and it arrived once it was released early June 2015. I very much recommend this book and method. I have a background in statistics and I currently work in a business area where the Bayesian approach is rarely considered as a possible solution. This is mainly due to computational complexity with larger datasets. The INLA method has been a real "game-changer" for us and now are very much considering approaches we had not thought as realistically possible. For those unfamiliar, the Integrated Nested Laplace
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.
3 of 3 people found the following review helpful.
The authors have done an excellent job. The book does not teach only theory
By Simon Sovoe
The authors have done an excellent job. The book does not teach only theory, but balance quite detail theory with real practical examples that put the theory in perspective. The book is extremely good for those interested in applying Bayesian approach to a large data set.
1 of 2 people found the following review helpful.
This book truly an excellent book for people who have no foundation in either ...
By Mark Ghamsary
This book truly an excellent book for people who have no foundation in either Bayesian or Satial statistics.
Congratulations to both authors !
Mark Ghamsary
Loma Libda University
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