中文版第202页讲HMM那节例一
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现在，当你放进硬币以后，如果机器处于可乐状态时它出来的就是可乐，而处于冰茶状态时出来的就是冰茶，于是我们便有了一个显示马尔科夫模型。但如果不是这样，他只是趋向于这么做。因此，我们需要符号化观测道德发射概率：
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WTF？到底是VMM还是HMM，如果以前不了解HMM的人读了这段话能够对理解有任何的帮助么？我认为反而只会增加困惑。而这居然是书中对HMM介绍的唯一一个例子，也就是说入门的人看到这里连一个最基本的感性认识都没有。下面是英语原文：
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Now, if, when you put in your coin, the machine always put out a cola
if it was in the cola preferring state and an iced tea when it was in the
iced tea preferring state, then we would have a visible Markov model. But instead, it only has a tendency to do this. So we need symbol emission probablities for the observations：
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其次我觉得这本书本身结构和叙述方面也不很好，依然以这里为例，说好了只有cola和iced_tea，然而下面的表却莫名奇妙的蹦出来一个lemonade。
一个原本简介漂亮的HMM居然会被安排成这样的形式介绍给初学者。作者和译者真是很差劲。
In probability theory, a probability mass function (pmf) is a function that gives the probability that a discrete random variable is exactly equal to some value. A pmf differs from a probability density function (pdf) in that the values of a pdf, defined only for continuous random variables, are not probabilities as such. Instead, the integral of a pdf over a range of possible values (a, b] gives the probability of the random variable falling within that range. See notation for the meaning of (a, b].
http://en.wikipedia.org/wiki/Probability_mass_function