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Unformatted text preview: 1 18.338J/16.394J: The Mathematics of Infinite Random Matrices Project Ideas Alan Edelman Handout #8, Thursday, September 30, 2004 A requirement of this course is that the students experiment with a random matrix problem that is of interest to them. Often these explorations take the shape of a little bit of theory and a little bit of computational experimentation. Some of you might already have some ideas that are relevant to your current research. Regardless, we thought we’d put together some ideas for projects. Feel free to adapt them based on your interests; if you want more information about a particular idea, please feel to contact us. Midterm project presentations are tentatively scheduled for October 28th and November 2nd as indicated on the course calendar. We will provide more details about this in a subsequent email to the class. The basic purpose of this midterm project is to get your feet wet thinking about a problem that is of interest to you using the tools you’ve learned about so far or to learn new tools for that purpose. Covariance Matrices in Signal Processing Sample covariance matrices come up in many signal processing applications such as adaptive filtering. Let G be the n × N “Gaussian” random matrix that we’ve encountered in class. In signal processing language, the Wishart matrix 1 ∗ W = GG (1) N is a rectangularly windowed estimator. In adaptive filtering applications, the covariance matrix that comes is a rectangularly windowed estimator....
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This note was uploaded on 11/07/2011 for the course AERO 16.26 taught by Professor Dimitribertsekas during the Fall '02 term at MIT.
 Fall '02
 DimitriBertsekas

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