Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework - 2013 PROJECT TITLE :Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework - 2013ABSTRACT: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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Group-Based Medium Access Control for IEEE 802.11n Wireless LANs - 2013 Mobile Relay Configuration in Data-Intensive Wireless Sensor Networks - 2013
