Information theory, multivariate dependence, and genetic network inference

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dc.creatorNemenman, Ilya
dc.date2004-06-07
dc.date.accessioned2026-07-07T08:17:25Z
dc.date.available2026-07-07T08:17:25Z
dc.descriptionWe define the concept of dependence among multiple variables using maximum entropy techniques and introduce a graphical notation to denote the dependencies. Direct inference of information theoretic quantities from data uncovers dependencies even in undersampled regimes when the joint probability distribution cannot be reliably estimated. The method is tested on synthetic data. We anticipate it to be useful for inference of genetic circuits and other biological signaling networks.
dc.description8 pages, 2 figures
dc.identifierhttps://arxiv.org/abs/q-bio/0406015
dc.identifierhttp://arxiv.org/abs/q-bio/0406015
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/134082
dc.subjectQuantitative Methods
dc.subjectInformation Theory
dc.subjectStatistics Theory
dc.subjectData Analysis, Statistics and Probability
dc.subjectGenomics
dc.titleInformation theory, multivariate dependence, and genetic network inference
dc.typetext

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