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dc.contributor.authorSanderson, Conrad
dc.contributor.authorCurtin, Ryan
dc.date.accessioned2020-07-30T03:53:15Z
dc.date.available2020-07-30T03:53:15Z
dc.date.issued2017
dc.identifier.issn2475-9066
dc.identifier.doi10.21105/joss.00365
dc.identifier.urihttp://hdl.handle.net/10072/395920
dc.description.abstractStatistical modelling of multivariate data through a convex mixture of Gaussians, also known as a Gaussian mixture model (GMM), has many applications in fields such as signal processing, econometrics, and pattern recognition (Bishop 2006). Each component (Gaus-sian) in a GMM is parameterised with a weight, mean vector (centroid), and covariance matrix.
dc.publisherThe Open Journal
dc.relation.ispartofissue18
dc.relation.ispartofjournalThe Journal of Open Source Software
dc.relation.ispartofvolume2
dc.subject.fieldofresearchApplied Statistics
dc.subject.fieldofresearchcode010401
dc.titlegmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and Expectation-Maximisation
dc.typeJournal article
dcterms.bibliographicCitationSanderson, C; Curtin, R, gmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and Expectation-Maximisation, The Journal of Open Source Software, 2017, 2 (18)
dc.date.updated2020-07-29T04:17:49Z
dc.description.versionPublished
gro.rights.copyright© 2017 Authors of JOSS papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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gro.griffith.authorSanderson, Conrad


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