Stationary Distribution in Markov Chains

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Covers the theory and applications of Markov chains, focusing on key concepts and properties.

Introduces linear algebra concepts, focusing on solving systems of linear equations and their representations.

Covers the theory and examples of diagonalizing matrices, focusing on eigenvalues, eigenvectors, and linear independence.

Explores eigenvalues and eigenvectors, demonstrating their importance in linear algebra and their application in solving systems of equations.

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Covers eigenvalues, eigenvectors, and the Fibonacci sequence, exploring their mathematical properties and practical applications.

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Covers linear applications, matrices, transformations, and the principle of superposition.

Explores eigenvalues, eigenvectors, diagonalization, and spectral theorem in linear algebra.