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Approximations of Cumulants of the Stochastic Power Law Logistic Model

Asymptotic approximations of the first three cumulants of the quasi-stationary distribution of the stochastic power law logistic model are derived. The results are based on a system of ODEs for the first three cumulants. We deviate from the classical moment closure approach by determining approximat...

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Autor principal: Nåsell, Ingemar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6976556/
https://www.ncbi.nlm.nih.gov/pubmed/31970522
http://dx.doi.org/10.1007/s11538-019-00687-w
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author Nåsell, Ingemar
author_facet Nåsell, Ingemar
author_sort Nåsell, Ingemar
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description Asymptotic approximations of the first three cumulants of the quasi-stationary distribution of the stochastic power law logistic model are derived. The results are based on a system of ODEs for the first three cumulants. We deviate from the classical moment closure approach by determining approximations without closing the system of equations. The approximations are explicit in the model’s parameters, conditions for validity of the approximations are given, magnitudes of approximation errors are given, and spurious solutions are easily detected and eliminated. In these ways, we provide improvements on previous results for this model.
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spelling pubmed-69765562020-02-03 Approximations of Cumulants of the Stochastic Power Law Logistic Model Nåsell, Ingemar Bull Math Biol Original Article Asymptotic approximations of the first three cumulants of the quasi-stationary distribution of the stochastic power law logistic model are derived. The results are based on a system of ODEs for the first three cumulants. We deviate from the classical moment closure approach by determining approximations without closing the system of equations. The approximations are explicit in the model’s parameters, conditions for validity of the approximations are given, magnitudes of approximation errors are given, and spurious solutions are easily detected and eliminated. In these ways, we provide improvements on previous results for this model. Springer US 2020-01-22 2020 /pmc/articles/PMC6976556/ /pubmed/31970522 http://dx.doi.org/10.1007/s11538-019-00687-w Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Original Article
Nåsell, Ingemar
Approximations of Cumulants of the Stochastic Power Law Logistic Model
title Approximations of Cumulants of the Stochastic Power Law Logistic Model
title_full Approximations of Cumulants of the Stochastic Power Law Logistic Model
title_fullStr Approximations of Cumulants of the Stochastic Power Law Logistic Model
title_full_unstemmed Approximations of Cumulants of the Stochastic Power Law Logistic Model
title_short Approximations of Cumulants of the Stochastic Power Law Logistic Model
title_sort approximations of cumulants of the stochastic power law logistic model
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6976556/
https://www.ncbi.nlm.nih.gov/pubmed/31970522
http://dx.doi.org/10.1007/s11538-019-00687-w
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