论文标题
比较频繁的和贝叶斯的周期性信号检测：统计错误的速率和对先验的敏感性
Comparing the frequentist and Bayesian periodic signal detection: rates of statistical mistakes and sensitivity to priors
论文作者
论文摘要
我们执行广泛的蒙特卡洛模拟，以系统地比较伦伯 - 刻录期刊的频繁主义和贝叶斯处理。目的是研究贝叶斯时期的搜索是否优于频繁的搜索效率，在检测效率上，如果是的，那么对先验的选择有多敏感，尤其是在事先证明的情况下（每当所采用的先验事先不匹配实际的物理对象的实际分布时，我们发现，贝叶斯和频繁的分析总是在I型和II类错误之间的权衡方面提供几乎相同的检测效率。如果频率先验不均匀，贝叶斯检测可能会揭示正式优势，但这仅导致$ \ sim 1 $ 1 $额外检测的信号。如果以前的规定（在实际统一的情况下采用了非统一），则可能会变成频繁分析的相反优势。最后，我们透露，如果没有针对I型错误（假阳性）进行校准，则该任务的贝叶斯因子似乎显得过于保守，因此在实践中需要进行这种校准。
We perform extensive Monte Carlo simulations to systematically compare the frequentist and Bayesian treatments of the Lomb--Scargle periodogram. The goal is to investigate whether the Bayesian period search is advantageous over the frequentist one in terms of the detection efficiency, how much if yes, and how sensitive it is regarding the choice of the priors, in particular in case of a misspecified prior (whenever the adopted prior does not match the actual distribution of physical objects). We find that the Bayesian and frequentist analyses always offer nearly identical detection efficiency in terms of their tradeoff between type-I and type-II mistakes. Bayesian detection may reveal a formal advantage if the frequency prior is nonuniform, but this results in only $\sim 1$ per cent extra detected signals. In case if the prior was misspecified (adopting nonuniform one over the actual uniform) this may turn into an opposite advantage of the frequentist analysis. Finally, we revealed that Bayes factor of this task appears rather overconservative if used without a calibration against type-I mistakes (false positives), thereby necessitating such a calibration in practice.
