Discrete-Time Markov Chains: Definitions

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Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

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.

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

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

Explores NISQ devices and IBM Q, covering noisy quantum circuits, qubit technologies, and quantum algorithm development.

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

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

Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.

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