A Markov chain can be used to model the evolution of a sequence of random events where probabilities for each depend solely on the previous event. Once a state in the sequence is observed, previous values are no longer relevant for the prediction of future values. Markov chains have many applications for modeling real-world phenomena in a myriad of disciplines including physics, biology, chemistry, queueing, and information theory.
instructor(s)
Corcoran, Jem
Primary Instructor
- Fall 2025 / Spring 2026
