Statistical Inference and Local Spatial Modelling: Living and Working with Uncertainty

Chris Brunsdon
Department of Geography, University of Leicester, Leicester, United Kingdom
cb179@le.ad.uk

Abstract: Uncertainty exists in all forms of spatial process modelling. Errors exist in data, the correct model form for the process is often uncertain and the actual process being modeled is itself random. This all suggests that there is a need to handle uncertainty in the modeling and data analysis process. One emerging approach to inference in this framework is that of Bayesian inference based on Monte Carlo Markov Chain simulations. Until the advent of these techniques, Bayesian approaches, although of theoretical interest were often computationally impractical. The simulation approach overcomes many of these problems and allows great flexibility in terms of questions that may be addressed. Here these methods will be reviewed, and in particular their application to spatial data analysis will be discussed, together with examples.
 

Keywords: Bayesian data analysis; Monte Carlo Markov Chain;Ceographically Varying Coefficients

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