Spatial Data Analysis Using Gaussian Markov Random Fields and Gaussian Processes

Kentaro Matsuura
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Kentaro Matsuura: HOXO-M Inc.

Chapter Chapter 12 in Bayesian Statistical Modeling with Stan, R, and Python, 2022, pp 285-329 from Springer

Abstract: Abstract We will learn about a Gaussian Markov random filed (GMRF), which can be considered as an extension of the state space model, and how to use it to analyze spatial data. It has a wide range of application and can be applied to one-dimensional data, two-dimensional grid data type, and geospatial map data. Later, we will see how a Gaussian process (GP) can be considered as a generalization of a GMRF. A GP can represent smooth functions, and usually gives high prediction performance.

Date: 2022
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DOI: 10.1007/978-981-19-4755-1_12

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