A Partition Theorem for a Randomly Selected Large Population

TL;DR

This paper presents a theorem that enables the partitioning of large random populations into stationary and non-stationary parts using stationary population identity, with practical applications summarized.

Contribution

It introduces a novel partition theorem for large populations based on stationary population identity, advancing understanding of population dynamics.

Findings

The theorem effectively separates stationary and non-stationary population components.

Practical applications of the theorem are summarized.

The approach provides a new tool for population analysis.

Abstract

We state and prove a theorem on the partitioning of a randomly selected large population into stationary and non-stationary components by using a property of stationary population identity. Applications of this theorem for practical purposes is summarized at the end.