Filling the gaps: Gaussian mixture models from noisy, truncated or incomplete samples

@article{Melchior2018FillingTG,
title={Filling the gaps: Gaussian mixture models from noisy, truncated or incomplete samples},
author={P. Melchior and A. Goulding},
journal={Astron. Comput.},
year={2018},
volume={25},
pages={183-194}
}

Astronomical data often suffer from noise and incompleteness. We extend the common mixtures-of-Gaussians density estimation approach to account for situations with a known sample incompleteness by simultaneous imputation from the current model. The method, called GMMis, generalizes existing Expectation-Maximization techniques for truncated data to arbitrary truncation geometries and probabilistic rejection processes, as long as they can be specified and do not depend on the density itself. The… CONTINUE READING