运用一些众所周知的数学知识，对于每个单词，可以生成一个“垃圾邮件指示性概率”。
Using well-known mathematics, it is possible to generate a "spam-indicative probability" for each word.
在“A Plan for Spam”（请参阅本文后面的 参考资料）中，Graham 提议建立垃圾邮件和非垃圾邮件单词的贝叶斯概率模型。
In "A Plan for Spam" (see Resources later in this article), Graham suggested building Bayesian probability models of spam and non-spam words.
从它推导出的功能组合出现在垃圾邮件中的概率高。
From that it is deduced which combination of features appears with high probability in spam messages.
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