文章基本信息

标题：Binary Hypothesis Testing with Learning of Empirical Distributions ⁎ ⁎
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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