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

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

Oct 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010ascl.soft10032b&link_type=abstract

Astrophysics Source Code Library, record ascl:1010.032

Astronomy and Astrophysics

Astrophysics

Scientific paper

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.

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