Continuous-Time Markov Chains: Definitions and State Probabilities

Related lectures (31)

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Covers examples of absorbing Markov chains in discrete time.

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

Discusses discrete-time Markov chains and absorption time in communication systems.

Explores a first-order Markov model using a sunny-rainy source example, demonstrating how past events influence future outcomes.

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

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

Explores Markov chains' properties, expectations, and recurrence in Poisson processes.

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.

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