{"title":"贝叶斯在市场营销中的应用","authors":"Greg M. Allenby, Peter E. Rossi","doi":"10.2139/ssrn.1356062","DOIUrl":null,"url":null,"abstract":"We review applications of Bayesian methods to marketing problems. Key aspects of marketing applications include the discreteness of response or outcome data and relatively large numbers of cross-sectional units, each with possibly low information content. The use of informative priors including hierarchical models is essential for successful Bayesian applications in marketing. Given the importance of the prior, it is important to assure flexibility in the prior specification. Non-standard likelihoods and flexible priors make marketing a very challenging area for Bayesian applications.","PeriodicalId":224732,"journal":{"name":"Chicago Booth Research Paper Series","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Bayesian Applications in Marketing\",\"authors\":\"Greg M. Allenby, Peter E. Rossi\",\"doi\":\"10.2139/ssrn.1356062\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We review applications of Bayesian methods to marketing problems. Key aspects of marketing applications include the discreteness of response or outcome data and relatively large numbers of cross-sectional units, each with possibly low information content. The use of informative priors including hierarchical models is essential for successful Bayesian applications in marketing. Given the importance of the prior, it is important to assure flexibility in the prior specification. Non-standard likelihoods and flexible priors make marketing a very challenging area for Bayesian applications.\",\"PeriodicalId\":224732,\"journal\":{\"name\":\"Chicago Booth Research Paper Series\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-03-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chicago Booth Research Paper Series\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.1356062\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chicago Booth Research Paper Series","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.1356062","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We review applications of Bayesian methods to marketing problems. Key aspects of marketing applications include the discreteness of response or outcome data and relatively large numbers of cross-sectional units, each with possibly low information content. The use of informative priors including hierarchical models is essential for successful Bayesian applications in marketing. Given the importance of the prior, it is important to assure flexibility in the prior specification. Non-standard likelihoods and flexible priors make marketing a very challenging area for Bayesian applications.