Ergodic Theory: Markov Chains

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Explores classification, communication, and irreducibility in Markov chains.

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

Explains the proof of the ergodic theorem and the concept of positive-recurrence in Markov chains.

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

Covers Mod.7 on continuous-time Markov chains, focusing on reversible chains and their applications in communication systems.

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

Covers the concept of invariant measures in Markov chains and their role in analyzing irreducible recurrent processes.

Covers reversible discrete-time Markov chains in communication models.

Explores Markov chains, focusing on homogeneous processes and stationary distributions, with practical exercises.

Covers the basic theory for continuous time Markov chains and discusses communication, hitting probabilities, recurrence, and transience.