15
6 Estimation for Lattice Models
6.1 Introduction
6.2 Sample Moments
6.3 The AR(1) Process on 
16 7 Kriging 7.1 Introduction 7.2 The Prediction Problem 7.3 Simple Kriging 7.4 Ordinary Kriging 7.5 Universal Kriging 7.6 Further Details for the Universal Kriging Predictor 7.7 Stationary Examples 7.8 Intrinsic Random Fields 7.9 Intrinsic Examples 7.10 Square Example 7.11 Kriging with Derivative Information 7.12 Bayesian Kriging 7.13 Kriging and Machine Learning 7.14 The Link Between Kriging and Splines 7.15 Reproducing Kernel Hilbert Spaces 7.16 Deformations Exercises
17 8 Additional Topics 8.1 Introduction 8.2 Log‐normal Random Fields 8.3 Generalized Linear Spatial Mixed Models (GLSMMs) 8.4 Bayesian Hierarchical Modeling and Inference 8.5 Co‐kriging 8.6 Spatial–temporal Models 8.7 Clamped Plate Splines 8.8 Gaussian Markov Random Field Approximations 8.9 Designing a Monitoring Network Exercises
18
Appendix A Mathematical Background
A.1 Domains for Sequences and Functions
A.2 Classes of Sequences and Functions
A.3 Matrix Algebra
A.4 Fourier Transforms
A.5 Properties of the Fourier Transform
A.6 Generalizations of the Fourier Transform
A.7 Discrete Fourier Transform and Matrix Algebra
A.8 Discrete Cosine Transform (DCT)
A.9 Periodic Approximations to Sequences
A.10 Structured Matrices in 
19 Appendix B A Brief History of the Spatial Linear Model and the Gaussian Process Approach B.1 Introduction B.2 Matheron and Watson B.3 Geostatistics at Leeds 1977–1987 B.4 Frequentist vs. Bayesian Inference
20 References and Author Index
21 Index
22 Wiley End User License Agreement
List of Tables
1 Chapter 1Table 1.1 Illustrative data 
