Explores communicating classes in Markov chains, distinguishing between transient and recurrent classes, and delves into the properties of these classes.
Explores the convergence of adjacency matrix powers and consensus theorem for primitive and stochastic matrices, emphasizing spectral properties and networked control systems.
Explores the concept of stationary distribution in Markov chains, discussing its properties and implications, as well as the conditions for positive-recurrence.
