Non-Gaussian processes

Non-Gaussian processes are types of random systems where fluctuations or variations do not follow the typical pattern of a normal (bell-shaped) distribution. Unlike Gaussian processes, which are characterized by symmetrical curves and predictable behaviors, non-Gaussian processes can have skewed, heavy-tailed, or more complex distributions. They often model real-world phenomena with rare but impactful events, such as financial crashes, earthquakes, or traffic spikes. Recognizing non-Gaussian behavior helps in better understanding, predicting, and managing systems where extreme or atypical outcomes are more common than standard models suggest.