Explores equilibrium in Markov Chains, covering invariant distributions, properties determination, and practical applications.
Explores Markov chains, their properties, and algorithmic applications, emphasizing information quantification and state monotonicity.
Explores the decomposition of Markov chains into communicating classes and the behavior of long-run averages.
Explores communicating classes in Markov chains, distinguishing between transient and recurrent classes, and delves into the properties of these classes.
Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.
Introduces continuous time Markov chains on a finite state space with exponential waiting times and jump probabilities.
