Stochastic Models for Communications

Related lectures (32)

Page 3 of 4

Explores recurrent states, invariant distributions, convergence to equilibrium, and PageRank algorithm.

Explores Markov chain games, hitting probabilities, and expected hitting times in a target set.

Covers the definitions and state probabilities of discrete-time Markov chains.

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

Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.

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

Covers stochastic models for binary transmission in communications systems.

Covers the definitions and state probabilities of discrete-time Markov chains.

Covers continuous-time Markov chains and Kolmogorov equations.

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