Multivariate Gaussian Distribution

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Explores common probability distributions, special distributions, and entropy concepts.

Explores independence, linear combinations of variables, and probability laws.

Explains multivariate Gaussian distribution properties and moment generating functions for random vectors.

Explores Gaussian vectors, moment generating functions, independence, density functions, affine transformations, and quadratic forms.

Covers the basics of linear algebra, including matrix operations and singular value decomposition.

Covers fundamental linear algebra concepts like vector spaces and eigenvalues.

Covers the method of diagonalizing a symmetric matrix using an orthogonal matrix.

Explores vector subspaces in R4, symmetric matrices, basis vectors, and canonical forms.

Covers random vectors, joint probability, and conditional probability in communication stochastic models.

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.