首页    期刊浏览 2024年10月06日 星期日
登录注册

文章基本信息

  • 标题:Binary Hypothesis Testing with Learning of Empirical Distributions ⁎ ⁎
  • 本地全文:下载
  • 作者:Aneesh Raghavan ; John S. Baras
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2021
  • 卷号:54
  • 期号:9
  • 页码:671-676
  • DOI:10.1016/j.ifacol.2021.06.128
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractBinary hypothesis testing with a single observer is considered. The true distributions of the observations under either hypothesis are unknown. Empirical distributions are estimated from observations. A sequence of detection problems are solved using the sequence of empirical distributions. The convergence of the information state and optimal detection cost under empirical distributions to the information state and optimal detection cost under the true distribution are shown. Simulation results are presented and are consistent with the results mentioned earlier.
  • 关键词:KeywordsHypothesis TestingEmpirical DistributionsStatistical Learning
国家哲学社会科学文献中心版权所有