论文标题
一种用于计算种群遗传学统计数据的更快，更准确的算法，需要第一类的stirl数字
A faster and more accurate algorithm for calculating population genetics statistics requiring sums of Stirling numbers of the first kind
论文作者
论文摘要
第一种种群的斯特林数量用于衍生几个种群遗传学统计数据，这反过来又可直接从DNA序列中测试进化假设。在这里，我们探讨了这些stirling数字的累积分布函数，该数字可以使用表示不完整的beta函数来实现总和的单个直接估计。该估计器启用了一种改进的方法，用于计算一个有用的统计量fu的$ f_s $的渐近估计。通过将计算从涉及斯特数字的术语总和减少到单个估计值，我们同时提高了准确性并大大提高速度。
Stirling numbers of the first kind are used in the derivation of several population genetics statistics, which in turn are useful for testing evolutionary hypotheses directly from DNA sequences. Here, we explore the cumulative distribution function of these Stirling numbers, which enables a single direct estimate of the sum, using representations in terms of the incomplete beta function. This estimator enables an improved method for calculating an asymptotic estimate for one useful statistic, Fu's $F_s$. By reducing the calculation from a sum of terms involving Stirling numbers to a single estimate, we simultaneously improve accuracy and dramatically increase speed.
