基于算法信息论框架下随机数生成器测试分析构建测试组合
Building test batteries based on analysing random number generator tests within the framework of algorithmic information theory
AI总结：
本文利用算法信息论框架分析随机数生成器测试，证明其可在传统统计方法失效时比较测试效力，并建议将基于字典的数据压缩测试纳入测试组合。
摘要：
本文考虑了随机数生成器的测试问题，并表明基于算法信息论的方法能够在现有数理统计方法无法区分测试的某些情况下，比较不同测试的效力。特别地，本文表明基于使用字典的数据压缩方法的测试应被纳入测试组合中。
英文摘要：
The problem of testing random number generators is considered and it is shown that an approach based on algorithmic information theory allows us to compare the power of different tests in some cases where the available methods of mathematical statistics do not distinguish between the tests. In particular, it is shown that tests based on data compression methods using dictionaries should be included in the test batteries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
