Difference: Papers20100623123101 (1 vs. 3)

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
Volume 1, No. 2, August 2005
DOI: 10.2104/ag050014

Date:

Authors: Monica Palaseanu-Lovejoy

Link: http://publications.epress.monash.edu/doi/pdf/10.2104/ag050014

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
Volume 1, No. 2, August 2005
DOI: 10.2104/ag050014

-- TWikiAdminUser - 2010-06-16

Date:

Authors: Monica Palaseanu-Lovejoy

Link: http://publications.epress.monash.edu/doi/pdf/10.2104/ag050014

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
Volume 1, No. 2, August 2005
DOI: 10.2104/ag050014

-- TWikiAdminUser - 2010-06-16