Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
Covers hitting probabilities in Markov chains with disjoint subsets, the function h(i), theorems, proofs, and expected time to hit calculations.
Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.
Explores a first-order Markov model using a sunny-rainy source example, demonstrating how past events influence future outcomes.
Covers concentration inequalities and sampling methods for estimating unknown distributions, with a focus on population infection rates.
Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.
