Coupling Theory is a mathematical framework used to study the behavior of stochastic processes, particularly in probability theory. It involves creating a joint probability space for two or more processes, allowing researchers to analyze their relationships and convergence properties. This theory is particularly useful in understanding how different random variables interact over time. In practical applications, Coupling Theory can be employed in various fields, including statistics, machine learning, and statistical physics. By establishing a coupling between processes, one can derive insights about their long-term behavior and make predictions about their future states, enhancing the understanding of complex systems.