The Adelman–López theorem

The Adelman–López theorem deals with how to understand complex probability distributions. It states that if you combine many independent random processes—each with predictable behaviors—the overall combined process will resemble a mixture of these individual behaviors, as the number of processes grows large. In essence, it provides a way to approximate complex, high-variability systems by understanding simpler, individual components. This theorem is valuable in fields like statistics and finance, where analyzing the aggregate behavior of numerous stochastic (random) factors helps in modeling and decision-making.