He said literature and the arts are critical to what he called normalization after decades of military rule.

他表示文学和艺术对于经过几十年军事统治后他所谓归一至关重要。

So in a normalized T-distribution, you're going to have a mean of 0.

所以在归一 T 中，均值为 0。

And so what's the normalization constant that I need to normalize it? 1.

那么，我需要归一常数是什么呢？1.

The distribution of all the T-statistics would be a normalized T-distribution.

所有 T 统计量都是归一 T 。

If you look at a normal distribution, a completely normalized normal distribution, it's mean is at 0.

如果你看一个，一个完全归一，它均值为 0。

Over here in a T-distribution, and this will actually be a normalized T-distribution right here because we subtracted out the mean.

在 T 中，这实际上是归一 T ，因为我们减去了均值。

And then we want to find an interval, so if this is a normalized T-distribution the mean is going to be 0.

然后我们想要找到一个区间，所以如果这是一个归一 T ，则平均值将为 0。

Then we find the squared distance from that expected value, and we, I guess you could call it, normalize it by the expected value.

然后我们找到与该期望值平方距离，我想你可以称之为， 用期望值对其进行归一。

And so sometimes we write them real neatly with the normalizing constants and place the mean and the variance in places that you can recognize very easily.

因此，有时我们会用归一常数将它们整齐地写出来，并将平均值和方差放在您很容易识别地方。

So e to the minus 1 half x bar minus mu squared over sigma squared over n. And I've gotten rid of my normalizing constants right here.

所以 e 到负 1 一半 x bar 减去 mu 平方超过 sigma 平方超过 n。我在这里删除了我归一常量。

And a lot of your priors on non-compact spaces, unbounded things like mu, turn out that you want to give a prior that can't be normalized quite often.

而你在非紧空间上很多先验，比如 mu 这样无界事物， 结果证明你想给出一个不能经常归一先验。

So the probability of getting a Z-value less than 1.65, or even in a completely normalized normal distribution, the probability of getting a value less than 1.65.

所以得到 Z 值小于 1. 65 概率，甚至在完全归一中， 得到值小于 1. 65 概率。

So I'll say pi p would be proportional to, so there's a normalizing constant in here, p to the alpha minus 1, 1 minus p, beta minus 1.

所以我会说 pi p 与 成比，所以这里有一个归一常数，p 到 alpha 减 1,1 减去 p， beta 减去 1。

So this conjugacy thing is great if you know about the distribution because it won't require you to keep track of any of your normalizing constants or solve that integral in the denominator at the end of the day.

所以如果你了解，这个共轭就很好了，因为它不需要你跟踪任何归一常数，也不需要你在一天结束时解决母中积。

Well, in a normalized normal distribution, I got a Z-table right over here, and we can assume everything is normal, especially the sampling distributions because our n is so big and also our proportion is not close to zero or one.

好吧，在归一中，我在这里有一个 Z 表， 我们可以假设一切都是常，尤其是抽样， 因为我们 n 太大而且我们比例不接近于零或一。