Exact, Recursive, Inference of Event Space Probability Law for Discrete Random Sets with Applications

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dc.contributor.authorSidiropoulos, N.en_US
dc.contributor.authorBaras, John S.en_US
dc.contributor.authorBerenstein, Carlos A.en_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T09:47:51Z
dc.date.available2007-05-23T09:47:51Z
dc.date.issued1991en_US
dc.description.abstractIn this paper we extend Choquet's result to obtain a recursive procedure for the computation of the underlying event-space probability law for Discrete Random Sets, based on Choquet's capacity functional. This is an important result, because it paves the way for the solution of statistical inference problems for Discrete Random Sets. As an example, we consider the Discrete Boolean Random Set with Radial Convex Primary Grains model, compute its capacity functional, and use our procedure to obtain a recursive solution to the problem of M-ary MAP hypothesis testing for the given model. The same procedure can be applied to the problem of ML model fitting. Various important probability functionals are computed in the process of obtaining the above results.en_US
dc.format.extent416195 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/5087
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1991-39en_US
dc.subjectdetectionen_US
dc.subjectestimationen_US
dc.subjectimage processingen_US
dc.subjectsignal processingen_US
dc.subjectcomputational complexityen_US
dc.subjectSystems Integrationen_US
dc.titleExact, Recursive, Inference of Event Space Probability Law for Discrete Random Sets with Applicationsen_US
dc.typeTechnical Reporten_US

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