dc.contributor.author | Edelman, Alan | |
dc.contributor.author | Rao, N. Raj | |
dc.date.accessioned | 2005-12-14T19:14:10Z | |
dc.date.available | 2005-12-14T19:14:10Z | |
dc.date.issued | 2006-01 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/30241 | |
dc.description.abstract | Random 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.sponsorship | Singapore-MIT Alliance (SMA) | en |
dc.format.extent | 83803 bytes | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.relation.ispartofseries | Computer Science (CS) | en |
dc.subject | Free probability | en |
dc.subject | random matrices | en |
dc.subject | stochastic eigen-inference | en |
dc.subject | rank estimation | en |
dc.subject | principal component analysis | en |
dc.title | Free Probability, Sample Covariance Matrices and Stochastic Eigen-Inference | en |
dc.type | Article | en |