{"title":"从噪声传感器数据估计函数:因子图方法","authors":"J. Barros, M. Tüchler","doi":"10.1109/CISS.2007.4298413","DOIUrl":null,"url":null,"abstract":"The combination of graphical models and belief propagation algorithms has found wide acceptance in the design of communication systems. We extend the general framework of joint source-channel decoding on graphs to account for estimation problems in which the goal is not to decode the entire data set but to estimate a function of the transmitted data. This problem is deemed relevant e.g. in the context of wireless sensor networks.","PeriodicalId":151241,"journal":{"name":"2007 41st Annual Conference on Information Sciences and Systems","volume":"260 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimating a Function from Noisy Sensor Data: A Factor Graph Approach\",\"authors\":\"J. Barros, M. Tüchler\",\"doi\":\"10.1109/CISS.2007.4298413\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The combination of graphical models and belief propagation algorithms has found wide acceptance in the design of communication systems. We extend the general framework of joint source-channel decoding on graphs to account for estimation problems in which the goal is not to decode the entire data set but to estimate a function of the transmitted data. This problem is deemed relevant e.g. in the context of wireless sensor networks.\",\"PeriodicalId\":151241,\"journal\":{\"name\":\"2007 41st Annual Conference on Information Sciences and Systems\",\"volume\":\"260 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 41st Annual Conference on Information Sciences and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CISS.2007.4298413\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 41st Annual Conference on Information Sciences and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISS.2007.4298413","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Estimating a Function from Noisy Sensor Data: A Factor Graph Approach
The combination of graphical models and belief propagation algorithms has found wide acceptance in the design of communication systems. We extend the general framework of joint source-channel decoding on graphs to account for estimation problems in which the goal is not to decode the entire data set but to estimate a function of the transmitted data. This problem is deemed relevant e.g. in the context of wireless sensor networks.
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