11
Gaussian processes (GPs)
Spatial and temporal statistics
Preface
1
Introduction to time series
2
Linear systems
3
ARMA models
4
Estimation and forecasting
5
Spectral analysis
6
Hidden Markov models (HMMs)
7
Dynamic linear models (DLMs)
8
State space modelling
9
Introduction to spatial data
10
Continuously varying random fields
11
Gaussian processes (GPs)
12
Spectral theory for GPs
13
Kriging
14
Lattice random fields
15
Spatial auto-regressive models
16
Inference for lattice models
17
Spatial point processes
18
Spatio-temporal models and data
References
Table of contents
11.1
Introduction
11
Gaussian processes (GPs)
11.1
Introduction
10
Continuously varying random fields
12
Spectral theory for GPs