Discusses the fundamentals of probability and stochastic processes, focusing on random variables, their properties, and applications in statistical signal processing.
Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.
