Markov Chains: Stopping Times

This lecture covers the concept of stopping times in Markov Chains, defining them as random variables that determine when a process should stop. It explains how stopping times are used in the context of Markov Chains, providing examples and illustrating the properties of stopping times. The lecture also delves into the Strong Markov Property, which states that the future behavior of a process, given a stopping time, is conditionally independent of its past. Additionally, it discusses the classification of communicating classes in Markov Chains as recurrent or transient, highlighting the necessary and sufficient conditions for each classification.