Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework

The authors 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. They 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.

Provided by: Institute of Electrical & Electronic Engineers Topic: Data Management Date Added: Jan 2013 Format: PDF

Find By Topic