👉 Heath math, developed by mathematician John Heath-Pearson, is a framework for analyzing and understanding the structure of discrete random processes, particularly those involving sequences of random variables. It focuses on the properties of these sequences through a set of invariants, known as "Heath invariants," which capture essential features of the process's behavior without requiring explicit knowledge of the underlying probability distribution. These invariants are defined in terms of the process's local structure and its interactions with neighboring elements, allowing for a more abstract and general approach to studying random processes. By identifying and analyzing these invariants, Heath math provides powerful tools for characterizing and classifying random processes, making it especially useful in fields like statistical physics, computer science, and machine learning, where understanding the intricate patterns within sequences is crucial.
