Markov Chains: Ergodicity and Stationary Distribution

Related lectures (31)

Covers the criterion for recurrence in infinite chains based on the expected number of visits in a state.

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

Explores birth and death chains, hitting probabilities, and expected game durations in Markov chains.

Explores hitting times in Markov chains through various examples and scenarios.

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

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

Explores probability inequalities, convergence types, and moment generating functions for distribution approximation.

Explores the Bonus Malus system for insurance premiums and Markov chain transition probabilities.

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

Covers the theory of continuous-time Markov chains, focusing on birth and death processes.

Covers conditional expectation, convergence of random variables, and the strong law of large numbers.

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