Gaussian Random Vectors

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Page 3 of 4

Explores symmetric matrices, diagonalization, and quadratic forms properties.

Covers non-negative definite matrices, covariance matrices, and Principal Component Analysis for optimal dimension reduction.

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Explores Gaussian processes, linear systems, transformations, and noise properties in multivariable control applications.

Explores continuous random variables and their properties, including support and cumulative distribution functions.

Discusses variance and covariance calculations, transformations of random variables, and mixtures in probability and statistics.

Covers elementary distributions like Bernoulli, Binomial, Geometric, Negative Binomial, Poisson, and Multinomial.

Explores statistical models, families of distributions, transformations, and their applications in probability theory.

Covers continuous random variables, probability density functions, and distributions, with practical examples.

Explores isometries in Euclidean spaces, including translations, rotations, and linear symmetries, with a focus on matrices.