Bayesian Prediction of Transformed Gaussian Random Fields

Simple item page
dc.contributor.authorOliveira, V. Deen_US
dc.contributor.authorKedem, Benjaminen_US
dc.contributor.authorShort, D.en_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T10:01:36Z
dc.date.available2007-05-23T10:01:36Z
dc.date.issued1996en_US
dc.description.abstractThe purpose of this work is to extend the methodology presented in Handock and Stein (1993) for prediction in Gaussian random fields to the case of transformed Gaussian random fields when the transformation is only known to belong to a parametric family. As the optimal predictor, the median of the Bayesian predictive distribution is used since the mean of this distribution does not exist for many commonly used nonlinear transformations. Monte Carlo integration is used for the approximation of the predictive density function, which is easy to implement in this framework. An application to spatial prediction of weekly rainfall amounts in Darwin Australia is presented.en_US
dc.format.extent839956 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/5755
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1996-36en_US
dc.subjectestimationen_US
dc.subjectimage processingen_US
dc.subjectstochastic systemsen_US
dc.subjectoptimal predictionen_US
dc.subjectBox-Cox transformationen_US
dc.subjectcross-validationen_US
dc.subjectMonte Carlo integrationen_US
dc.subjectrainfall,en_US
dc.titleBayesian Prediction of Transformed Gaussian Random Fieldsen_US
dc.typeTechnical Reporten_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
TR_96-36.pdf
Size:
820.27 KB
Format:
Adobe Portable Document Format
Download

Collections

Institute for Systems Research Technical Reports