Inference of a Dyadic Measure and its Simplicial Geometry from Binary Feature Data and Application to Data Quality

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Detalles Bibliográficos
Publicado en:	arXiv.org (Apr 29, 2017), p. n/a
Autor Principal:	Ness, Linda
Publicado:	Cornell University Library, arXiv.org
Materias:	Geometry Data processing Mathematical analysis Bayesian analysis Differential geometry Homology Balances (scales) Privacy Decision making Statistics Parameters Statistical inference Representations Multiscale analysis
Acceso en liña:	Citation/Abstract Full text outside of ProQuest
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