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Bayesian Spatial Analysis

  • Chris Brunsdon
Reference work entry

Abstract

This chapter outlines the key ideas of Bayesian spatial data analysis, together with some practical examples. An introduction to the general ideas of Bayesian inference is given, and in particular the key rôle of MCMC approaches is emphasized. Following this, techniques are discussed for three key types of spatial data: point data, point-based measurement data, and area data. For each of these, examples of appropriate kinds of spatial data are considered and examples of their use are also provided. The chapter concludes with a discussion of the advantages that Bayesian spatial analysis has to offer as well as considering some of the challenges that this relatively new approach is faced with.

Keywords

Posterior Distribution Markov Chain Monte Carlo Prior Distribution Bayesian Inference Spatial Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Environmental SciencesUniversity of LiverpoolLiverpoolUK

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