Stochastic Models for Communications: Continuous-Time Stochastic Processes - Stationarity

Related lectures (22)

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

Covers the theory and application of the Wiener filter for discrete-time stochastic processes.

Explores the concept of separation of variables in quantum mechanics and its application in solving differential equations.

Explores the Wiener filter for discrete-time stochastic processes and its applications.

Covers continuous-time Markov chains, focusing on reversible chains and their properties.

Covers solving linear inhomogeneous differential equations and finding their general solutions using the method of variation of constants.

Explores examples of absorbing chains in discrete-time Markov chains, focusing on transition probabilities.

Introduces the basics of quantum mechanics, covering wave functions, operators, and the uncertainty principle.

Covers the Runge-Kutta method for approximating solutions of differential equations.

Explores linear systems, convergence, and solving methods with a focus on CPU time and memory requirements.