Explores Markov chain games, hitting probabilities, and expected hitting times in a target set.
Explores risk evaluation, equivalent value, and insurance premium in lotteries, as well as discount rate adjustments.
Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.
Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
Explores probability convergence, discussing conditions for random variable sequences to converge and the uniqueness of convergence.
Explores NISQ devices and IBM Q, covering noisy quantum circuits, qubit technologies, and quantum algorithm development.
Explores a first-order Markov model using a sunny-rainy source example, demonstrating how past events influence future outcomes.
Explores probability inequalities, convergence types, and moment generating functions for distribution approximation.
