Applied Probability & Stochastic Processes

Related lectures (55)

Covers eigenvalues, eigenvectors, characteristic polynomials, and eigenspaces for square matrices.

Covers graphical models for probability distributions and factor graphs representation.

Explores matrix similarity and diagonalization rules, emphasizing eigenvectors and distinct eigenvalues.

Covers symmetric matrices, eigenvalues, and diagonalization process for spectral theorem applications.

Explores Bourgain's theorem on sparsest cut in graphs, emphasizing semimetrics and cut optimization.

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.

Explores network sampling consistency, models, and graph dynamics in real-life scenarios.

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Covers random variables, variances, and covariance, as well as the probability in random graphs.

Covers the equations of Kolmogorov for continuous-time Markov chains.

Explores linear combinations, matrix-vector product, and matrix equation solutions.

Covers continuous-time Markov chains and Kolmogorov equations in stochastic communication models.

Covers reversible discrete-time Markov chains and their concept of reversibility.

Explores Goldstone bosons and the Higgs mechanism, revealing how spontaneous symmetry breaking generates mass for gauge bosons.

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