Neyman–Scott process

The Neyman-Scott process is a stochastic model used to describe the formation of clustered point patterns.

Originally developed for modeling galaxy distributions by J. Neyman and Elizabeth L. Scott in 1952,[1] it provides a framework for understanding phenomena characterized by clustering.

It is applied across diverse fields like astronomy, epidemiology,[2] ecology, and materials science, particularly where events occur in groups rather than independently.

These parent points are typically latent,[2] meaning they are not directly observable.

These offspring points are the observable elements of the Neyman-Scott process.