{"title":"非参数贝叶斯:文化之间的桥梁","authors":"Arman Oganisian, J. Roy","doi":"10.1353/obs.2021.0005","DOIUrl":null,"url":null,"abstract":"Abstract:In this commentary, we assess the cultural fit of Bayesian nonparametrics in light of advances in the field since Breiman's 2001 article. We argue that Bayesian nonparametrics synthesizes desirable elements of the data modeling and algorithmic cultures to yield new insights and methodological improvements. We discuss how these methods have been combined with identification strategies from the causal inference literature to do flexible inference for interpretable target parameters.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"7 1","pages":"175 - 178"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nonparametric Bayes: A Bridge Between Cultures\",\"authors\":\"Arman Oganisian, J. Roy\",\"doi\":\"10.1353/obs.2021.0005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract:In this commentary, we assess the cultural fit of Bayesian nonparametrics in light of advances in the field since Breiman's 2001 article. We argue that Bayesian nonparametrics synthesizes desirable elements of the data modeling and algorithmic cultures to yield new insights and methodological improvements. We discuss how these methods have been combined with identification strategies from the causal inference literature to do flexible inference for interpretable target parameters.\",\"PeriodicalId\":74335,\"journal\":{\"name\":\"Observational studies\",\"volume\":\"7 1\",\"pages\":\"175 - 178\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Observational studies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1353/obs.2021.0005\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Observational studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1353/obs.2021.0005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Abstract:In this commentary, we assess the cultural fit of Bayesian nonparametrics in light of advances in the field since Breiman's 2001 article. We argue that Bayesian nonparametrics synthesizes desirable elements of the data modeling and algorithmic cultures to yield new insights and methodological improvements. We discuss how these methods have been combined with identification strategies from the causal inference literature to do flexible inference for interpretable target parameters.