Marchenko–Pastur distribution

The Marchenko–Pastur distribution is a concept from random matrix theory, often applied in statistics and finance. It describes the behavior of eigenvalues (certain special values associated with matrices) of large, random matrices that emerge from real-world data, such as correlations in financial markets. This distribution helps analysts understand the limits and structures of data-driven models, particularly in high-dimensional settings. Essentially, it provides insights into how data points relate to each other and what patterns might exist, allowing for better predictions and analyses in various fields, including economics and signal processing.