Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
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.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
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.
