
An Efficient Model Selection for Gaussian Mixture Model in a Bayesian Framework
In order to cluster or partition data, we often use ExpectationandMaxi...
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AutoGMM: Automatic Gaussian Mixture Modeling in Python
Gaussian mixture modeling is a fundamental tool in clustering, as well a...
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Planeextraction from depthdata using a Gaussian mixture regression model
We propose a novel algorithm for unsupervised extraction of piecewise pl...
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Robust Mixture Modeling using Weighted Complete Estimating Equations
Mixture modeling that takes account of potential heterogeneity in data i...
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A Bayesian Method for Estimating Uncertainty in Excavated Material
This paper proposes a method to probabilistically quantify the moments (...
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Mixture Density Network Estimation of Continuous Variable Maximum Likelihood Using Discrete Training Samples
Mixture Density Networks (MDNs) can be used to generate probability dens...
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Note on approximating the Laplace transform of a Gaussian on a complex disk
In this short note we study how well a Gaussian distribution can be appr...
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Gaussian Mixture Estimation from Weighted Samples
We consider estimating the parameters of a Gaussian mixture density with a given number of components best representing a given set of weighted samples. We adopt a density interpretation of the samples by viewing them as a discrete Dirac mixture density over a continuous domain with weighted components. Hence, Gaussian mixture fitting is viewed as density reapproximation. In order to speed up computation, an expectationmaximization method is proposed that properly considers not only the sample locations, but also the corresponding weights. It is shown that methods from literature do not treat the weights correctly, resulting in wrong estimates. This is demonstrated with simple counterexamples. The proposed method works in any number of dimensions with the same computational load as standard Gaussian mixture estimators for unweighted samples.
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