{"title":"贝叶斯非线性回归在水文模型中的应用","authors":"George Kuczera","doi":"10.1016/0141-1195(89)90044-2","DOIUrl":null,"url":null,"abstract":"<div><p>A suite of FORTRAN 77 computer programs implementing Bayesian nonlinear regression is described. These programs, developed to cope with complex hydrologic models, are used to infer model parameters, test model structure and make predictions of future response. The programs are fully interactive with features including: interactive parameter optimization using the Gauss-Marquardt algorithm; run-time editing of user options; availability of a general error model; use of prior information; and joint fitting of multiple-response data.</p></div>","PeriodicalId":100043,"journal":{"name":"Advances in Engineering Software (1978)","volume":"11 3","pages":"Pages 149-155"},"PeriodicalIF":0.0000,"publicationDate":"1989-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0141-1195(89)90044-2","citationCount":"14","resultStr":"{\"title\":\"An application of Bayesian nonlinear regression to hydrologic models\",\"authors\":\"George Kuczera\",\"doi\":\"10.1016/0141-1195(89)90044-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A suite of FORTRAN 77 computer programs implementing Bayesian nonlinear regression is described. These programs, developed to cope with complex hydrologic models, are used to infer model parameters, test model structure and make predictions of future response. The programs are fully interactive with features including: interactive parameter optimization using the Gauss-Marquardt algorithm; run-time editing of user options; availability of a general error model; use of prior information; and joint fitting of multiple-response data.</p></div>\",\"PeriodicalId\":100043,\"journal\":{\"name\":\"Advances in Engineering Software (1978)\",\"volume\":\"11 3\",\"pages\":\"Pages 149-155\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1989-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/0141-1195(89)90044-2\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Engineering Software (1978)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/0141119589900442\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Engineering Software (1978)","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/0141119589900442","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An application of Bayesian nonlinear regression to hydrologic models
A suite of FORTRAN 77 computer programs implementing Bayesian nonlinear regression is described. These programs, developed to cope with complex hydrologic models, are used to infer model parameters, test model structure and make predictions of future response. The programs are fully interactive with features including: interactive parameter optimization using the Gauss-Marquardt algorithm; run-time editing of user options; availability of a general error model; use of prior information; and joint fitting of multiple-response data.