Discrete-Time Markov Chains: Definitions

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

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

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

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

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

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

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

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

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

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Explores first passage times, strong Markov property, and state recurrence/transience in Markov chains.

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