Spatial and Spatio-temporal Bayesian Models with R - INLA [Hardcover]

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

Dedication iii

Preface ix

1 Introduction 1

1.1 Why spatial and spatio-temporal statistics? 1

1.2 Why do we use Bayesian methods for modelling spatial and spatio-temporal structures? 2

1.3 Why INLA? 3

1.4 Datasets 3

2 Introduction to 21

2.1 The language 21

2.2 objects 22

2.3 Data and session management 34

2.4 Packages 35

2.5 Programming in 36

2.6 Basic statistical analysis with 39

3 Introduction to Bayesian Methods 53

3.1 Bayesian Philosophy 53

3.2 Basic Probability Elements 57

3.3 Bayes Theorem 62

3.4 Prior and Posterior Distributions 64

3.5 Working with the Posterior Distribution 66

3.6 Choosing the Prior Distribution 68

4 Bayesian computing 83