In this article we define a procedure which corrects an incoherent probability assessment on a finite domain by exploiting a geometric property of L1-distance (known also as Manhattan distance) and mixed integer programming. L1-distance minimization does not produce, in general, a unique solution but rather a corrected assessment that could result an imprecise probability model. We propose a correction method for the merging of two separate assessments whose direct juxtaposition could be incoherent, and for the revision of beliefs where the core of the assessment must remain unchanged. A prototypical example on antidoping analysis guides the reader through this article to explain the various procedures.
Efficient L1-based probability assessments correction: algorithms and applications to belief merging and revision
BAIOLETTI, Marco;CAPOTORTI, Andrea
2015
Abstract
In this article we define a procedure which corrects an incoherent probability assessment on a finite domain by exploiting a geometric property of L1-distance (known also as Manhattan distance) and mixed integer programming. L1-distance minimization does not produce, in general, a unique solution but rather a corrected assessment that could result an imprecise probability model. We propose a correction method for the merging of two separate assessments whose direct juxtaposition could be incoherent, and for the revision of beliefs where the core of the assessment must remain unchanged. A prototypical example on antidoping analysis guides the reader through this article to explain the various procedures.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
