Spatial and spatio-temporal Bayesian models with R-INLA

Spatial and spatio-temporal Bayesian models with R-INLA / by Marta Blangiardo and Michela Cameletti.

Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a muchneeded, practically oriented & innovative presentation of the combination ofBayesian methodology and spatial statistics. The authors combine an introduction toBayesian theory and methodology with a focus on the spatial and sp...

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Bibliographic Details
Main Author: Blangiardo, Marta
Other Authors: Cameletti, Michela
Format: eBook
Language:English
Published: Chichester, West Sussex : John Wiley and Sons, Inc., 2015.
Subjects:
Bayesian statistical decision theory.
Spatial analysis (Statistics)
Asymptotic distribution (Probability theory)
R (Computer program language)
spatial analysis.
MATHEMATICS > Applied.
MATHEMATICS > Probability & Statistics > General.
Bayesian statistical decision theory
Online Access:Click for online access
Table of Contents:
  • Title Page; Copyright; Table of Contents; Dedication; Preface; Chapter 1: Introduction; 1.1 Why spatial and spatio-temporal statistics?; 1.2 Why do we use Bayesian methods for modeling spatial and spatio-temporal structures?; 1.3 Why INLA?; 1.4 Datasets; References; Chapter 2: Introduction to R; 2.1 The R language; 2.2 R objects; 2.3 Data and session management; 2.4 Packages; 2.5 Programming in R; 2.6 Basic statistical analysis with R; References; Chapter 3: Introduction to Bayesian methods; 3.1 Bayesian philosophy; 3.2 Basic probability elements; 3.3 Bayes theorem.
  • 3.4 Prior and posterior distributions3.5 Working with the posterior distribution; 3.6 Choosing the prior distribution; References; Chapter 4: Bayesian computing; 4.1 Monte Carlo integration; 4.2 Monte Carlo method for Bayesian inference; 4.3 Probability distributions and random number generation in R; 4.4 Examples of Monte Carlo simulation; 4.5 Markov chain Monte Carlo methods; 4.6 The integrated nested Laplace approximations algorithm; 4.7 Laplace approximation; 4.8 The R-INLA package; 4.9 How INLA works: step-by-step example; References.
  • Chapter 5: Bayesian regression and hierarchical models5.1 Linear regression; 5.2 Nonlinear regression: random walk; 5.3 Generalized linear models; 5.4 Hierarchical models; 5.5 Prediction; 5.6 Model checking and selection; References; Chapter 6: Spatial modeling; 6.1 Areal data
  • GMRF; 6.2 Ecological regression; 6.3 Zero-inflated models; 6.4 Geostatistical data; 6.5 The stochastic partial differential equation approach; 6.6 SPDE within R-INLA; 6.7 SPDE toy example with simulated data; 6.8 More advanced operations through the inla.stack function; 6.9 Prior specification for the stationary case.
  • 6.10 SPDE for Gaussian response: Swiss rainfall data6.11 SPDE with nonnormal outcome: malaria in the Gambia; 6.12 Prior specification for the nonstationary case; References; Chapter 7: Spatio-temporal models; 7.1 Spatio-temporal disease mapping; 7.2 Spatio-temporal modeling particulate matter concentration; References; Chapter 8: Advanced modeling; 8.1 Bivariate model for spatially misaligned data; 8.2 Semicontinuous model to daily rainfall; 8.3 Spatio-temporal dynamic models; 8.4 Space-time model lowering the time resolution; References; Index; End User License Agreement.

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