论文标题
常见主义者与贝叶斯分析：互相关作为Ligo-Virgo随机背景搜索的（近似）足够的统计量
Frequentist versus Bayesian analyses: Cross-correlation as an (approximate) sufficient statistic for LIGO-Virgo stochastic background searches
论文作者
论文摘要
足够的统计数据是数据的组合，即可以在不丢失信息的情况下重写似然函数。根据数据量减少，在贝叶斯分析中使用足够的统计数据作为初步步骤，当从模型参数的后验分布中采样时，效率显着提高。在这里，我们表明，互相关统计量及其方差的频率积分是用于地面搜索随机重力波背景的足够统计数据。足够的统计数据是近似值，因为一个人在弱信号近似中起作用，并使用每个检测器中自动相关功率的测量估计值。使用分析和数值计算，我们证明了Ligo-Virgo的混合频率bayesian参数估计分析等同于完全贝叶斯分析。这项工作缩小了Ligo-Virgo文献中的差距，并建议进行其他搜索。
Sufficient statistics are combinations of data in terms of which the likelihood function can be rewritten without loss of information. Depending on the data volume reduction, the use of sufficient statistics as a preliminary step in a Bayesian analysis can lead to significant increases in efficiency when sampling from posterior distributions of model parameters. Here we show that the frequency integrand of the cross-correlation statistic and its variance are approximate sufficient statistics for ground-based searches for stochastic gravitational-wave backgrounds. The sufficient statistics are approximate because one works in the weak-signal approximation and uses measured estimates of the auto-correlated power in each detector. Using analytic and numerical calculations, we prove that LIGO-Virgo's hybrid frequentist-Bayesian parameter estimation analysis is equivalent to a fully Bayesian analysis. This work closes a gap in the LIGO-Virgo literature, and suggests directions for additional searches.
