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The code is written in pure python (developed and tested in 2.7), parallelized with `multiprocessing`, and is capable of performing density estimation with millions of samples and thousands of model components on machines with sufficient memory.
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More details are in the paper listed below. Please cite it if you make use of this code:
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```
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@ARTICLE{pygmmis,
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author = {{Melchior}, P. and {Goulding}, A.~D.},
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title = "{Filling the gaps: Gaussian mixture models from noisy, truncated or incomplete samples}",
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journal = {Astronomy and Computing},
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volume = "25",
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pages = {183 - 194},
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year = "2018",
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month = oct,
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doi = {10.1016/j.ascom.2018.09.013},
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url = {https://www.sciencedirect.com/science/article/pii/S2213133718300489},
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archivePrefix = "arXiv",
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eprint = {1611.05806},
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primaryClass = "astro-ph.IM"
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}
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```
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More details are in the paper listed in the file `CITATION.cff`.
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