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.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Delves into Markov chains by analyzing a scenario with two fleas moving in opposite directions, exploring transition matrices and probabilities over time.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
