Abstract
We consider the problem of statistical parameter estimation when the data are uncertain and described by belief functions. An extension of the Expectation-Maximization (EM) algorithm, called the Evidential EM (E2M) algorithm, is described and shown to maximize a generalized likelihood function. This general procedure provides a simple mechanism for estimating the parameters in statistical models when observed data are uncertain. The method is illustrated using the problem of univariate normal mean and variance estimation from uncertain data.
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Denœux, T. (2010). Maximum Likelihood from Evidential Data: An Extension of the EM Algorithm. In: Borgelt, C., et al. Combining Soft Computing and Statistical Methods in Data Analysis. Advances in Intelligent and Soft Computing, vol 77. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14746-3_23
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DOI: https://doi.org/10.1007/978-3-642-14746-3_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14745-6
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