MAXIMUM LIKELIHOOD ESTIMATION FROM UNCERTAIN DATA IN THE
BELIEF FUNCTION FRAMEWORK
ABSTRACT:
We consider the problem of pa...
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Maximum likelihood estimation from uncertain data in the belief function framework

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Maximum likelihood estimation from uncertain data in the belief function framework

  1. 1. MAXIMUM LIKELIHOOD ESTIMATION FROM UNCERTAIN DATA IN THE BELIEF FUNCTION FRAMEWORK ABSTRACT: We consider the problem of parameter estimation in statistical models in the case where data are uncertain and represented as belief functions. The proposed method is based on the maximization of a generalized likelihood criterion, which can be interpreted as a degree of agreement between the statistical model and the uncertain observations. We propose a variant of the EM algorithm that iteratively maximizes this criterion. As an illustration, the method is applied to uncertain data clustering using finite mixture models, in the cases of categorical and continuous attributes. ECWAY TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE CELL: +91 98949 17187, +91 875487 2111 / 3111 / 4111 / 5111 / 6111 VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com

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