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Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework
Jan. 2013 (vol. 25 no. 1)
pp. 119-130
Thierry Denoeux, Université de Technologie de Compiègne, CNRS, Compiègne
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.
Index Terms:
Data models,Bayesian methods,Clustering algorithms,Uncertainty,Hidden Markov models,Probability distribution,Probability density function,mixture models,Uncertain data mining,Dempster-Shafer theory,Evidence theory,clustering,EM algorithm
Thierry Denoeux, "Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 1, pp. 119-130, Jan. 2013, doi:10.1109/TKDE.2011.201
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