Stochastic Models for Communications

Related lectures (32)

Page 1 of 4

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

Covers the classification of states in Markov chains.

Introduces continuous time Markov chains on a finite state space with exponential waiting times and jump probabilities.

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

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

Covers applied probability, Markov chains, and stochastic processes, including transition matrices, eigenvalues, and communication classes.

Covers the classification of states in discrete-time Markov chains.

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

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

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