Advanced spatial modeling with stochastic partial differential equations using R and INLA

Content: PreambleWhat this book is and isn\'t The Integrated Nested Laplace Approximation and the R-INLA package Introduction The INLA method A simple example Additional arguments and control options Manipulating the posterior marginals Advanced features Introduction to spatial modeling 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 More than one likelihood Coregionalization model Joint modeling: Measurement error model Copying part of or the entire linear predictor Point processes and preferential sampling Introduction Including a covariate in the log-Gaussian Cox process Geostatistical inference under preferential sampling Spatial non-stationarity Explanatory variables in the covariance The Barrier model Barrier model for noise data in Albacete (Spain) Risk assessment using non-standard likelihoods Survival analysis Models for extremes Space-time models Discrete time domain Continuous time domain Lowering the resolution of a spatio-temporal model Conditional simulation: Combining two meshes 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