Stochastic Processes: Generation and Embedding

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Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.

Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.

Covers linear systems, diagonal and triangular matrices, and LU factorization.

Introduces Markov chains, covering basics, generation algorithms, and applications in random walks and Poisson processes.

Explores the properties and examples of diagonalizable matrices, emphasizing the relationship between eigenvectors and eigenvalues.

Explores the equivalence between different properties of linear transformations represented by matrices and various matrix operations.

Covers the application of power calculation in matrices, focusing on reducing the matrix to calculate powers efficiently.

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Covers the generation of Markov processes and Markov chains, including transition matrices and stochastic matrices.