Markov Chains: Reversibility and Stationary Distribution

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Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.

Explores the theory of quantum jumps in open quantum systems and their simulation.

Explores memorylessness in distributions, stationary processes, and estimation using MLE.

Explores variance, covariance, and practical applications in statistics and probability.

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

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

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

Covers the abstract structure of measure-preserving systems and aims to understand their classification and ergodic decompositions.

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