So we were installing what's called an " eddy covariance tower, " which is a series of instruments that measures the exchange of greenhouse gases, like carbon dioxide and methane, between the land and the atmosphere.

所以我们安装了一个被称为“涡度协塔”装置，这种装置由多种仪器构成，用于测量地面与大气温室气体交换，例如二和甲烷。

So this is, I mentioned that before, variance-invariance, covariance regularization.

所以这就是我之前提到，不变性协正则。

Instead of looking for direct physical contact, it looks for statistical co-occurrence, basically covariance.

它不寻找直接物理接触，而是寻找统计上现，本质上是协。

Now, this right here-- so everything we've learned right now-- this right here is the covariance, or this is an estimate of the covariance of X and Y. Now what is this over here?

现在，这里内容——我们刚刚学到——这就是协，或者说是X和Y协一个估计值。 那么这里这个是什么呢？

And then we do the covariance between the vertical movements of wind and the concentration of gas.

然后我们计算风垂直运动和气体浓度之间协。

Well, you could view this as the covariance of X with X. But we've actually already seen this.

好吧，你可以将这视为X与自身协。 但实际上我们已经见过这个概念了。

That means variance, invariance, covariance, regularization.

那意味着、不变性、协、正则。

It's called an eddy covariance tower.

它被称为涡流协塔。

The covariance of a random variable with itself is really just the variance of that random variable.

一个随机变量与自身协其实就是该随机变量。

Gosselin: So on the tower for eddy covariance, in principle, there's two main instruments: one instrument that measures the concentration of gas that we're interested in in the air and [another] that uses infrared.

Gosselin：原则上，在涡流协塔上有两种主要仪器：一种测量空气中我们感兴趣气体浓度，[另一种]使用红外线。

If they both go up together, they would have a positive variance or they both go down together.

如果它们一起增加，它们就会有正协，或者它们一起减少。

And if we kept doing this, let's say for the entire population this happened, then it would make sense that they have a negative covariance.

如果我们继续这样做，假设这种情况发生在整个总体中， 那么它们具有负协就说得通了。

And the degree to which they do it together will tell you the magnitude of the covariance.

它们一起变程度将告诉你协大小。

Now we don't know the entire covariance, we only have one sample here of this random variable.

现在我们不知道整个协，我们这里只有一个随机变量样本。

I want to connect to this definition of covariance to everything we've been doing with least squared regression.

我想将协这个定义与我们一直在做最小二乘回归内容联系起来。

Hopefully that gives you a little bit of intuition about what the covariance is trying to tell us.

希望这能给你一点关于协想要告诉我们直觉。

And really it's just kind of a fun math thing to do to show you all of these connections, and where, really, the definition of covariance really becomes useful.

这其实只是一个有趣数学小知识，展示所有这些联系，并说明协定义在何处真正变得有用。