So that's my probability distribution function.

这就是我的概率分布函数。

The probability of getting any x, and it's a class of probability distribution functions.

得到任意x的概率，是一类概率分布函数。

But that's my discrete probability distribution function.

但那是我的离散概率分布函数。

So let's say I have a discrete probability distribution function.

所以假设我有一个离散的概率分布函数。

And you don't know the probability distribution functions for any of those things.

而且您不知道任何这些事情的概率分布函数。

Well, based on how we thought about the probability distribution functions for the discrete random variable, you'd say OK, let's see.

好吧，根据我们对离散随机变量的概率分布函数的看法， 你会说好吧， 让我们看看。

So now I think you can see where the probability distribution function starts to become a little bit more interesting.

所以现在我想你可以看到概率分布函数从哪里开始变得更有趣了。

So if you said that x is equal to the number on unfair dice that's described by this probability distribution function.

因此，如果您说 x 等于此概率分布函数描述的不公平骰子上的数字。

And let's say I don't know what the actual probability distribution function for this is, but I'll draw one and then we'll interpret it.

假设我不知道实际的概率分布函数是什么，但我会画一个然后我们会解释它。

So it's important to realize that a probability distribution function, in this case for a discrete random variable, they all have to add up to 1.

所以重要的是要认识到概率分布函数，在这种情况下对于离散随机变量， 它们都必须加起来为 1。

Now what I'm going to do here, instead of just taking samples of this random variable that's described by this probability distribution function, I'm going to take samples of it.

现在我要做的是，我不只是对这个概率分布函数描述的随机变量进行抽样， 而是对它进行抽样。