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dc.contributor.authorEdelman, Alan
dc.contributor.authorRao, N. Raj
dc.date.accessioned2005-12-14T19:14:10Z
dc.date.available2005-12-14T19:14:10Z
dc.date.issued2006-01
dc.identifier.urihttp://hdl.handle.net/1721.1/30241
dc.description.abstractRandom matrix theory is now a big subject with applications in many disciplines of science, engineering and finance. This talk is a survey specifically oriented towards the needs and interests of a computationally inclined audience. We include the important mathematics (free probability) that permit the characterization of a large class of random matrices. We discuss how computational software is transforming this theory into practice by highlighting its use in the context of a stochastic eigen-inference application.en
dc.description.sponsorshipSingapore-MIT Alliance (SMA)en
dc.format.extent83803 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.relation.ispartofseriesComputer Science (CS)en
dc.subjectFree probabilityen
dc.subjectrandom matricesen
dc.subjectstochastic eigen-inferenceen
dc.subjectrank estimationen
dc.subjectprincipal component analysisen
dc.titleFree Probability, Sample Covariance Matrices and Stochastic Eigen-Inferenceen
dc.typeArticleen


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