{"title":"非高斯随机参数化","authors":"P. Dewilde","doi":"10.1109/spsympo51155.2020.9593873","DOIUrl":null,"url":null,"abstract":"The paper addresses the question of parametrization with, independent parameters for a multi-variable, non-Gaussian set of stochastic variables, based on higher order moments. The issue is particularly relevant for the construction of low-complexity models that meet measured correlation data between powers of the variables. It turns out that such a parametrization exists in the case where the correlation data is stricty ordered by increasing degree, and the paper shows in outline how it can be constructed.","PeriodicalId":380515,"journal":{"name":"2021 Signal Processing Symposium (SPSympo)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On Non-Gaussian Stochastic Parametrization\",\"authors\":\"P. Dewilde\",\"doi\":\"10.1109/spsympo51155.2020.9593873\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper addresses the question of parametrization with, independent parameters for a multi-variable, non-Gaussian set of stochastic variables, based on higher order moments. The issue is particularly relevant for the construction of low-complexity models that meet measured correlation data between powers of the variables. It turns out that such a parametrization exists in the case where the correlation data is stricty ordered by increasing degree, and the paper shows in outline how it can be constructed.\",\"PeriodicalId\":380515,\"journal\":{\"name\":\"2021 Signal Processing Symposium (SPSympo)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 Signal Processing Symposium (SPSympo)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/spsympo51155.2020.9593873\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Signal Processing Symposium (SPSympo)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/spsympo51155.2020.9593873","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The paper addresses the question of parametrization with, independent parameters for a multi-variable, non-Gaussian set of stochastic variables, based on higher order moments. The issue is particularly relevant for the construction of low-complexity models that meet measured correlation data between powers of the variables. It turns out that such a parametrization exists in the case where the correlation data is stricty ordered by increasing degree, and the paper shows in outline how it can be constructed.