  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.
