Queueing Theory: M/G/∞ Queue

Related lectures (24)

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

Covers arrival and departure processes, Poisson distributions, service times, and system performance evaluation.

Covers the Poisson process in detail, focusing on the probability law and its applications.

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

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

Covers examples of absorbing Markov chains in discrete time.

Explores the M/G/infinity queue model for systems with infinite capacity and general service times.

Covers the Poisson process and its probability law in communication systems.

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

Covers deterministic and stochastic rainfall models in water resources engineering, including generation, calibration, and spatially explicit models.