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 can be generated that require large efforts to eliminate, and 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.
journal_name
Bull Math Bioljournal_title
Bulletin of mathematical biologyauthors
Nåsell Idoi
10.1007/s11538-017-0321-2subject
Has Abstractpub_date
2017-09-01 00:00:00pages
2088-2108issue
9eissn
0092-8240issn
1522-9602pii
10.1007/s11538-017-0321-2journal_volume
79pub_type
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