{"title":"通过累积忽视实现瞬间封闭","authors":"T. Matis, I. Guardiola","doi":"10.3888/TMJ.12-2","DOIUrl":null,"url":null,"abstract":"In this article, we introduce the package Moment Closure, which may be used to generate closure differential equations and closure approximations of the cumulants (moments) of a nonlinear stochastic compartmental model with Markov transitions. Specifically, this package defines the pair of functions MomentClosureSystem and MomentClosurePlots that achieves moment closure through the neglect of high-order cumulants. We demonstrate the application of these functions through the analysis of several test models. In select cases, the resulting cumulant approximations are compared across neglect levels and to exact answers.","PeriodicalId":91418,"journal":{"name":"The Mathematica journal","volume":"12 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Achieving Moment Closure through Cumulant Neglect\",\"authors\":\"T. Matis, I. Guardiola\",\"doi\":\"10.3888/TMJ.12-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this article, we introduce the package Moment Closure, which may be used to generate closure differential equations and closure approximations of the cumulants (moments) of a nonlinear stochastic compartmental model with Markov transitions. Specifically, this package defines the pair of functions MomentClosureSystem and MomentClosurePlots that achieves moment closure through the neglect of high-order cumulants. We demonstrate the application of these functions through the analysis of several test models. In select cases, the resulting cumulant approximations are compared across neglect levels and to exact answers.\",\"PeriodicalId\":91418,\"journal\":{\"name\":\"The Mathematica journal\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Mathematica journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3888/TMJ.12-2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Mathematica journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3888/TMJ.12-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this article, we introduce the package Moment Closure, which may be used to generate closure differential equations and closure approximations of the cumulants (moments) of a nonlinear stochastic compartmental model with Markov transitions. Specifically, this package defines the pair of functions MomentClosureSystem and MomentClosurePlots that achieves moment closure through the neglect of high-order cumulants. We demonstrate the application of these functions through the analysis of several test models. In select cases, the resulting cumulant approximations are compared across neglect levels and to exact answers.