Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA

Preamble

What this book is and isn't

2. The Integrated Nested Laplace Approximation and the R-INLA package

3. Introduction

   The INLA method

   A simple example

   Additional arguments and control options

   Manipulating the posterior marginals

   Advanced features

4. Introduction to spatial modeling

5. Introduction

   The SPDE approach

   A toy example

   Projection of the random field

   Prediction

   Triangulation details and examples

   Tools for mesh assessment

   Non-Gaussian response: Precipitation in Parana

6. More than one likelihood

7. Coregionalization model

   Joint modeling: Measurement error model

   Copying part of or the entire linear predictor

8. Point processes and preferential sampling

9. Introduction

   Including a covariate in the log-Gaussian Cox process

   Geostatistical inference under preferential sampling

10. Spatial non-stationarity

11. Explanatory variables in the covariance

    The Barrier model

    Barrier model for noise data in Albacete (Spain)

12. Risk assessment using non-standard likelihoods

13. Survival analysis

    Models for extremes

14. Space-time models

15. Discrete time domain

    Continuous time domain

    Lowering the resolution of a spatio-temporal model

    Conditional simulation: Combining two meshes

16. Space-time applications

Space-time coregionalization model

Dynamic regression example

Space-time point process: Burkitt example

Large point process dataset

Accumulated rainfall: Hurdle Gamma model

List of symbols and notation

Packages used in the book