XDGMM: eXtreme Deconvolution Gaussian Mixture Modeling

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Ada Coda
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XDGMM: eXtreme Deconvolution Gaussian Mixture Modeling

Post by Ada Coda » Fri Sep 01, 2017 2:28 am

XDGMM: eXtreme Deconvolution Gaussian Mixture Modeling

Abstract: XDGMM uses Gaussian mixtures to do density estimation of noisy, heterogenous, and incomplete data using extreme deconvolution (XD) algorithms which is compatible with the scikit-learn machine learning methods. It implements both the astroML and Bovy et al. (2011) algorithms, and extends the BaseEstimator class from scikit-learn so that cross-validation methods work. It allows the user to produce a conditioned model if values of some parameters are known.

Credit: Holoien, Thomas W.-S.; Marshall, Philip J.; Wechsler, Risa H.

Site: https://github.com/tholoien/XDGMM
https://ui.adsabs.harvard.edu/#abs/2017AJ....153..249H

Bibcode: 2017ascl.soft08026H

Preferred citation method: https://ui.adsabs.harvard.edu/#abs/2017AJ....153..249H and a link to https://github.com/tholoien/XDGMM

ID: ascl:1708.026

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