An Alternative to Moment Closure

TL;DR

This paper introduces an alternative method to traditional moment closure techniques, providing explicit, asymptotic approximations of cumulants that improve accuracy and simplicity in stochastic model analysis.

Contribution

The paper presents a new approach that yields explicit asymptotic cumulant approximations, addressing limitations of existing moment closure methods.

Findings

Improved accuracy in cumulant approximations for stochastic models

Explicit formulas derived for the Verhulst and SIR models

Errors in earlier moment closure applications are identified and corrected

Abstract

Moment closure methods are widely used to analyze mathematical models. They are specifically geared toward derivation of approximations of moments of stochastic models, and of similar quantities in other models. The methods possess several weaknesses: Conditions for validity of the approximations are not known, magnitudes of approximation errors are not easily evaluated, spurious solutions are generated that require large efforts to eliminate, expressions for the approximations are in many cases too complex to be useful. We describe an alternative method that provides improvements in these regards. The new method leads to asymptotic approximations of the first few cumulants that are explicit in the model's parameters. We analyze the univariate stochastic logistic Verhulst model and a bivariate stochastic epidemic SIR model with the new method. Errors that were made in early applications of moment closure to the Verhulst model are explained and corrected.