And if you do that you'll end up getting the Poisson distribution.

如果你这样做，你最终会得到泊松分布。

And this is important to our derivation of the Poisson distribution.

这对我们推导泊松分布很重要。

But it's a probability density function just like we studies the Poisson distribution.

但它是一个概率密度函数，就像我们研究泊松分布一样。

Just like the binomial distribution is and the Poisson distribution, it's based on a bunch of parameters.

就像二项分布和泊松分布一样，它基于一组参数。

Before we proved that as we take the limit as this number right here, the number of intervals approaches infinity that this becomes the Poisson distribution.

之前我们证明了，当我们这里取极限为这个数时，区间数趋近于无穷大，这就成了泊松分布。

And just a little aside, just to move forward with this video, there's two assumptions we need to make because we're going to study the Poisson distribution.

顺便说一句，为了继续播放这个视频，我们需要做出两个假设，因为我们要研究泊松分布。

And this is really interesting because a lot of times people give you the formula for the Poisson distribution and you can kind of just plug in the numbers and use it.

这真的很有趣，因为很多时候人们会给你泊松分布的公式，你可以插入数字并使用它。

In this example, k was 2 and you had exactly 2 terms. So once we know those two things we're now ready to derive the Poisson distribution and I'll do that in the next video.

在此示例中， k 为 2，您正好有 2 个项。 因此，一旦我们知道了这两件事， 我们就可以准备推导泊松分布了， 我将在下一个视频中进行介绍。