Stochastic Models for Communications

Related lectures (30)

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Covers the generation of Markov processes and Poisson processes in stochastic simulation.

Explores the theory of point processes and their applications to extremes, emphasizing the Laplace functional and Kallenberg's theorem.

Covers the fundamentals of stochastic models for communications, focusing on Markov chains and Poisson processes.

Explores the vibration of diatomic AB molecules and Fourier series, emphasizing equations of motion and Dirichlet's theorem.

Explores point processes in spatial analysis, focusing on spatial object dissemination and pattern detection.

Covers the introduction to Quantum Chaos, classical chaos, sensitivity to initial conditions, ergodicity, and Lyapunov exponents.

Covers the estimation of R in Poisson processes, focusing on marking points and convergence.

Covers the definitions and examples of continuous-time stochastic processes.

Covers De Moivre's formula for finding roots of complex numbers and the concept of complex exponential.