{"title":"多业务通信网络通信量参数的多元正态分布近似","authors":"В.А. Баранов, О.В. Крюков, С.А. Покацкий, И.О. Сушенцов","doi":"10.36622/vstu.2022.88.2.002","DOIUrl":null,"url":null,"abstract":"Для решения задачи представления многомерной функции распределения плотности вероятности, характеризующей наблюдаемые процессы в мультисервисной сети связи рассмотрен вариант ее аппроксимации смесью многомерных нормальных распределений с разным числом компонент.\n To solve the problem of representing a multidimensional probability density distribution function characterizing the observed processes in a multiservice communication network (MCN), a variant of its approximation by a mixture of multidimensional normal distributions with a different number of components is considered. This makes it possible to more accurately approximate the empirical distributions characterizing the observations.","PeriodicalId":331043,"journal":{"name":"СИСТЕМЫ УПРАВЛЕНИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"APPROXIMATION OF TRAFFIC PARAMETERS VALUES OF A MULTISERVICE COMMUNICATION NETWORK BY A MIXTURE OF MULTIVARIATE NORMAL DISTRIBUTIONS WITH A DIFFERENT NUMBER OF COMPONENTS\",\"authors\":\"В.А. Баранов, О.В. Крюков, С.А. Покацкий, И.О. Сушенцов\",\"doi\":\"10.36622/vstu.2022.88.2.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Для решения задачи представления многомерной функции распределения плотности вероятности, характеризующей наблюдаемые процессы в мультисервисной сети связи рассмотрен вариант ее аппроксимации смесью многомерных нормальных распределений с разным числом компонент.\\n To solve the problem of representing a multidimensional probability density distribution function characterizing the observed processes in a multiservice communication network (MCN), a variant of its approximation by a mixture of multidimensional normal distributions with a different number of components is considered. This makes it possible to more accurately approximate the empirical distributions characterizing the observations.\",\"PeriodicalId\":331043,\"journal\":{\"name\":\"СИСТЕМЫ УПРАВЛЕНИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"СИСТЕМЫ УПРАВЛЕНИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.36622/vstu.2022.88.2.002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"СИСТЕМЫ УПРАВЛЕНИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36622/vstu.2022.88.2.002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
APPROXIMATION OF TRAFFIC PARAMETERS VALUES OF A MULTISERVICE COMMUNICATION NETWORK BY A MIXTURE OF MULTIVARIATE NORMAL DISTRIBUTIONS WITH A DIFFERENT NUMBER OF COMPONENTS
Для решения задачи представления многомерной функции распределения плотности вероятности, характеризующей наблюдаемые процессы в мультисервисной сети связи рассмотрен вариант ее аппроксимации смесью многомерных нормальных распределений с разным числом компонент.
To solve the problem of representing a multidimensional probability density distribution function characterizing the observed processes in a multiservice communication network (MCN), a variant of its approximation by a mixture of multidimensional normal distributions with a different number of components is considered. This makes it possible to more accurately approximate the empirical distributions characterizing the observations.