{"title":"从示例中构建贝叶斯因子树","authors":"F. Palmieri, G. Romano, P. Rossi, D. Mattera","doi":"10.1109/CIP.2010.5604232","DOIUrl":null,"url":null,"abstract":"A criterion based on mutual information among variables is proposed for building a bayesian tree from a finite number of examples. The factor graph, in Forney-style form, can be used as an associative memory that performs probabilistic inference in data fusion applications. The procedure is explained with the aid of a fully-described example.","PeriodicalId":171474,"journal":{"name":"2010 2nd International Workshop on Cognitive Information Processing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Building a bayesian factor tree from examples\",\"authors\":\"F. Palmieri, G. Romano, P. Rossi, D. Mattera\",\"doi\":\"10.1109/CIP.2010.5604232\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A criterion based on mutual information among variables is proposed for building a bayesian tree from a finite number of examples. The factor graph, in Forney-style form, can be used as an associative memory that performs probabilistic inference in data fusion applications. The procedure is explained with the aid of a fully-described example.\",\"PeriodicalId\":171474,\"journal\":{\"name\":\"2010 2nd International Workshop on Cognitive Information Processing\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 2nd International Workshop on Cognitive Information Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIP.2010.5604232\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 2nd International Workshop on Cognitive Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIP.2010.5604232","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A criterion based on mutual information among variables is proposed for building a bayesian tree from a finite number of examples. The factor graph, in Forney-style form, can be used as an associative memory that performs probabilistic inference in data fusion applications. The procedure is explained with the aid of a fully-described example.
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