{"title":"多模型判别的贝叶斯优化设计准则","authors":"F. Z. Labbaf, H. Talebi","doi":"10.18869/acadpub.jsri.9.1.1","DOIUrl":null,"url":null,"abstract":". The problem of obtaining the optimum design, which is able to discriminate between several rival models has been considered in this paper. We give an optimality-criterion, using a Bayesian approach. This is an exten-sion of the Bayesian KL-optimality to more than two models. A modification is made to deal with nested models. The proposed Bayesian optimality criterion is a weighted average, where the weights are corresponding probabilities of models to let them be true. We consider these probabilities coming from a Poisson distribution.","PeriodicalId":422124,"journal":{"name":"Journal of Statistical Research of Iran","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bayesian Optimum Design Criterion for Multi Models Discrimination\",\"authors\":\"F. Z. Labbaf, H. Talebi\",\"doi\":\"10.18869/acadpub.jsri.9.1.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\". The problem of obtaining the optimum design, which is able to discriminate between several rival models has been considered in this paper. We give an optimality-criterion, using a Bayesian approach. This is an exten-sion of the Bayesian KL-optimality to more than two models. A modification is made to deal with nested models. The proposed Bayesian optimality criterion is a weighted average, where the weights are corresponding probabilities of models to let them be true. We consider these probabilities coming from a Poisson distribution.\",\"PeriodicalId\":422124,\"journal\":{\"name\":\"Journal of Statistical Research of Iran\",\"volume\":\"57 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Statistical Research of Iran\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18869/acadpub.jsri.9.1.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Research of Iran","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18869/acadpub.jsri.9.1.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bayesian Optimum Design Criterion for Multi Models Discrimination
. The problem of obtaining the optimum design, which is able to discriminate between several rival models has been considered in this paper. We give an optimality-criterion, using a Bayesian approach. This is an exten-sion of the Bayesian KL-optimality to more than two models. A modification is made to deal with nested models. The proposed Bayesian optimality criterion is a weighted average, where the weights are corresponding probabilities of models to let them be true. We consider these probabilities coming from a Poisson distribution.