Papers20100623123101 < AI_GEOSTATS

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*Title:* Bayesian Automating Fitting Functions for Spatial Predictions

*Date:* 1 July 2005

<strong>Authors: </strong>Monica Palaseanu-Lovejoy

<strong>Link: </strong> <a target="_blank" href="http://publications.epress.monash.edu/doi/pdf/10.2104/ag050014">http://publications.epress.monash.edu/doi/pdf/10.2104/ag050014</a>

*Abstract:*

A Bayesian predictive model for automating mapping of background radiation has the advantage of fully accounting for all uncertainties in the inferred data. Ten training datasets of background radiation were used to set up the model. The model is robust for data containing only close outliers but fails to accurately predict values when the input data is contaminated with extreme outliers, which are the result of a different random underlying process than the background data. For an integrated decision support system for automating mapping when data contamination is expected, a two stage approach is required in which background data are modeled with one set of equations and the contaminated data with a different set of equations.

Reference

Applied GIS<br />Volume 1, No. 2, August 2005 <br />DOI: 10.2104/ag050014

Topic revision: r3 - 2010-08-13 - 21:23:43 - TheresiaFreska

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