The covariance matrix has many interesting properties, and it can be found in mixture models, component analysis, Kalman filters, and more. Developing an intuition for how the covariance matrix operates is useful in understanding its practical implications. This article will focus on a few important properties, associated proofs, and then some interesting practical applications, i.e., extracting transformed polygons from a Gaussian mixture's covariance matrix.
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Added by Rohan Kotwani on May 26, 2019 at 7:30am — No Comments