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Stirling's Approximation for n!

斯特林近似式(n!)

When evaluating distribution functions for statistics, it is often necessary to evaluate the factorials of sizable numbers, as in the binomial distribution:

在统计学中评估分布函数时,常常需要计算较大数的阶乘,例如二项分布:

A helpful and commonly used approximate relationship for the evaluation of the factorials of large numbers is Stirling's approximation:

对于大数的阶乘估算,有一个很有帮助且常用的近似关系,即斯特林公式:

A slightly more accurate approximation is the following

一个稍更准确的近似是以下内容

but in most cases the difference is small. This additional term does give a way to assess whether the approximation has a large error.

但大多数情况下,这种差异很小。这个额外的项提供了一种评估近似值是否具有较大误差的方法。

Stirling's approximation is also useful for approximating the log of a factorial, which finds application in evaluation of entropy in terms of multiplicity, as in the Einstein solid. The log of n! is

斯特林近似也常用于近似阶乘的对数,这在用简并度表达熵时有应用,如在爱因斯坦固体中。n!的对数是

but the last term may usually be neglected so that a working approximation is

但最后一项通常可以忽略,因此一个近似表达为

Shroeder gives a numerical evaluation of the accuracy of the approximations

Shroeder 给出了一种对近似精度的数值评估
1
1
.922
7.7%
0
-1
...
10
3628800
3598696
.83%
15.1
13.0
13.8%
100
9 x 10157
9 x 10157
.083%
364
360
.89%
Relation to Gamma Function
与伽马函数的关系
Index

Statistics concepts

Reference
Schroeder
App. B
Ch 2
索引 统计概念 参考 Schröder 附录 B 第2章
 
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