Markov processes, a type of random dynamical processes, have a variety of applications with rich and interesting phenomena that are fully accessible through tools and methods from linear algebra. For instance, a random walk on a graph is the typical example of a Markov process. In particular, the random walker moves from vertex to vertex along edges based on independent rolls of a die. In the course, we will develop the theory of Markov processes and consider applications related to data science such as Markov chain Monte Carlo (MCMC) and Bayesian inference.

The textbooks are available electronically through the OSU library.