EM Algorithm for Maximum Likelihood Estimation by Non-Precise Information

The EM algorithm is a powerful tool for computing maximum likelihood estimates with incomplete data. This package will help to applying EM algorithm based on triangular and trapezoidal fuzzy numbers (as two kinds of incomplete data). A method is proposed for estimating the unknown parameter in a parametric statistical model when the observations are triangular or trapezoidal fuzzy numbers. This method is based on maximizing the observed-data likelihood defined as the conditional probability of the fuzzy data; for more details and formulas see Denoeux (2011) .


Reference manual

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1.0 by Abbas Parchami, 3 years ago

Browse source code at https://github.com/cran/EM.Fuzzy

Authors: Abbas Parchami (Department of Statistics , Faculty of Mathematics and Computer , Shahid Bahonar University of Kerman , Kerman , Iran)

Documentation:   PDF Manual  

LGPL (>= 3) license

Imports FuzzyNumbers, DISTRIB

See at CRAN