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  • 标题:On Divergence Measures Leading to Jeffreys and Other Reference Priors
  • 本地全文:下载
  • 作者:Ruitao Liu ; Arijit Chakrabarti ; Tapas Samanta
  • 期刊名称:Bayesian Analysis
  • 印刷版ISSN:1931-6690
  • 电子版ISSN:1936-0975
  • 出版年度:2014
  • 卷号:9
  • 期号:2
  • 页码:331-370
  • DOI:10.1214/14-BA862
  • 语种:English
  • 出版社:International Society for Bayesian Analysis
  • 摘要:The paper presents new measures of divergence between prior and posterior which are maximized by the Jeffreys prior. We provide two methods for proving this, one of which provides an easy to verify sufficient condition. We use such divergences to measure information in a prior and also obtain new objective priors outside the class of Bernardo’s reference priors.
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