Extreme Deconvolution: Density Estimation using Gaussian Mix

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owlice
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Extreme Deconvolution: Density Estimation using Gaussian Mixtures in the Presence of Noisy, Heterogeneous and Incomplete

Post by owlice » Mon Oct 18, 2010 3:12 pm

Extreme Deconvolution: Density Estimation using Gaussian Mixtures in the Presence of Noisy, Heterogeneous and Incomplete Data

Abstract: Extreme-deconvolution is a general algorithm to infer a d-dimensional distribution function from a set of heterogeneous, noisy observations or samples. It is fast, flexible, and treats the data's individual uncertainties properly, to get the best description possible for the underlying distribution. It performs well over the full range of density estimation, from small data sets with only tens of samples per dimension, to large data sets with hundreds of thousands of data points.

Credit: Bovy, Jo; Hogg, David W.; Roweis, Sam T.

Site: https://github.com/jobovy/extreme-deconvolution
http://adsabs.harvard.edu/abs/2012ApJ...744..195C

Bibcode: 2010ascl.soft10032B

ID: ascl:1010.032
Last edited by Ada Coda on Tue Dec 11, 2018 6:43 am, edited 1 time in total.
Reason: Updated code entry.
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Re: Extreme Deconvolution: Density Estimation using Gaussian

Post by owlice » Sat Mar 14, 2015 6:22 am

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