一个通俗易懂的解释，摘录如下：
Let's say you have input data x and you want to classify the data into labels y. A generative model learns the joint probability distribution p(x,y) and a discriminative model learns the conditional probability distribution p(y|x) - which you should read as 'the probability of y given x'.
Here's a really simple example. Suppose you have the following data in the form (x,y):
(1,0), (1,0), (2,0), (2, 1)
p(x,y) is
y=0 y=1-----------x=1 | 1/2 0x=2 | 1/4 1/4
p(y|x) is
y=0 y=1-----------x=1 | 1 0x=2 | 1/2 1/2
If you take a few minutes to stare at those two matrices, you will understand the difference between the two probability distributions.
The distribution p(y|x) is the natural distribution for classifying a given example x into a class y, which is why algorithms that model this directly are called discriminative algorithms. Generative algorithms model p(x,y), which can be tranformed into p(y|x) by applying Bayes rule and then used for classification. However, the distribution p(x,y) can also be used for other purposes. For example you could use p(x,y) to generate likely (x,y) pairs.
From the description above you might be thinking that generative models are more generally useful and therefore better, but it's not as simple as that. This paper is a very popular reference on the subject of discriminative vs. generative classifiers, but it's pretty heavy going. The overall gist is that discriminative models generally outperform generative models in classification tasks.
另一个解释，摘录如下：
- 判别模型Discriminative Model，又可以称为条件模型，或条件概率模型。估计的是条件概率分布(conditional distribution)， p(class|context)。
- 生成模型Generative Model，又叫产生式模型。估计的是联合概率分布（joint probability distribution），p(class, context)=p(class|context)*p(context)。
其它参考资料：
- 判别模型和生成模型的比较
- 另一个比较
- 生成模型
- 判别模型
- 概率图模型之生成模型与判别模型
- 判别模型 和 生成模型