October 2021
Intermediate to advanced
289 pages
8h 31m
Chinese
Content preview from 数据科学中的实用统计学(第2版)
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70
｜
第
2
章
这行代码会根据指数分布生成
100
个随机数，该指数分布中每个时段事件数量的均值是
0.2
。你可以用它来模拟服务电话之间的
100
个时间间隔
，单位为分钟，其中平均电话呼入
速率为每分钟
0.2
次。
在对泊松分布或指数分布的模拟研究中，一个关键假设是事件发生率
λ
在所考虑的时段中
是保持不变的。但从全局意义上说，这个假设很少能站住脚。例如，道路或数据网络上的
流量在每天的不同时段或者每周的不同日子都是变化的。不过，时段或空间区域通常可以
细分为足够同质化的片段，在这些片段中的分析或模拟都是有效的。
2.12.3
估计故障率
在很多应用中，事件发生率
λ
是已知的或者可以根据以前的数据估计出来。但是，对于罕
见的事件来说，并非总是如此。例如，飞机发动机故障相当罕见（谢天谢地），对于一种
特定类型的发动机，几乎没有数据能作为估计故障间隔时间的基础。没有数据，就没有估
计事件发生率的依据。但是，你可以做出某种猜测：如果
20
小时之后还没有任何事件发
生，你就能确定事件发生率不会是每小时
1
次
。通过模拟或者直接计算概率，你可以估计
出各种假设事件的发生率，还可以估计出一个阈值（事件发生率基本不会落到这个阈值之
下）。如果有一些数据，但还不足以提供精确可靠的发生率估计，就可以对各种发生率进
行一个拟合优度检验（参见
3.9
节）
，以确定它们与观测数据的拟合程度。
2.12.4
韦布尔分布
在很多情况下，随着时间的变化，事件发生率并不能保持恒定。如果发生率变化的时间间
隔远远大于通常的事件时间间隔，那么没有问题。正如前面说的那样
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.