Expectation Maximization of Gausian Mixture Models in VTK
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3218
Expectation maximization (EM) is a common technique for estimating the parameters of a model after having collected observations of data generated by the model. We first explain the algorithm, then present our impelementation. We focus on estimation of the parameters of a Gaussian Mixture Model (GMM). The implementation is written in the VTK framework and is provided as a new class, vtkExpectationMaximization.

The code is hosted here: http://github.com/daviddoria/ExpectationMaximization for the time being.

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Categories: Density Estimation, Parameter Techniques, Probability
Keywords: parameter estimation, GMM, EM
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