It'll take you from knowing nothing to being able to fully apply Bayes' theorem or multiple linear regression.

它将带你从一无所知到能够完全应用贝叶斯定理或多元性回归。

And he wrestled with this and he came up with a little bit of mathematics that we now call Bayes' theorem.

他为此进行了思考，并得出了我们现在称为贝叶斯定理的一点数学成果。

Each answer to that question gives you a new Bayes factor, a new thing that you multiply by your odds.

这个问题的每个答案你一个新的贝叶斯因子，一个你乘以你的赔率的新东西。

Bayes's theorem is crucial for everything from the scientific method to evaluating legal evidence and progress in artificial intelligence.

贝叶斯定理对于从科学方法到评估法律证据和人工智能的进步等一切至关重要。

Soon, Bayes got a tip that a horse fitting White Stockings' description was stabled at a tavern in Whitechapel.

很快，贝叶斯得到索，一匹符合怀特·斯托克林描述的马被安置在怀特柴泼区的一家酒馆里。

For this case, we do have a Bayes rule, once more.

对于这种情况，我们再次有贝叶斯规则。

Several machine learning algorithms, such as support vector machine (SVM), k-nearest neighbour (kNN), logistic regression (LR), naive Bayes and ensemble learning, were compared to model the healthy and HLB-infected samples after parameter optimization.

比较了几种机器学习法，如支持向量机（SVM）、k近邻法（kNN）、辑回归（LR）、朴素贝叶斯以及集成学习，在参数优化后对健康和黄龙病感染样本进行建模。

So my old joke to this class is, class dismissed, I've just showed you what Bayes is, that's it, write down a likelihood, multiply by a prior, and you're done.

所以我对这门课开的老笑话是，类被解雇，我刚刚向你展示了贝叶斯是什么，就是这样，写下一个似然，乘以一个先验， 你就完成了。

So a discussion of vaccine safety proceeds to Bayes's theorem, a procedure for refining one's judgment of probabilities as new evidence comes to light.

因此， 疫苗安全性的讨论将涉及到贝叶斯定理，这是一种随着新证据的出现而完善概率判断的程序。

What that would mean is that our  Bayes factor is 90 instead of 10, the test is doing more work for us.

这意味着我们的贝叶斯因子是90而不是10，测试为我们做了更多的工作。

So this plot is meant to show usage of Bayes.

所以这个图是为了展示贝叶斯的用法。

In the context of Bayes' theorem, this value also has a special name, it's the " likelihood" .

在贝叶斯定理的背景下，这个值也有一个特殊的名称，它是“似然”。

So Bayes' theorem says probability of A given B can be formed from the conditionals of B given A and the margin on A, the probability distribution on A.

所以贝叶斯定理说 A 定 B 的概率可以由定 A 的 B 的条件和 A 上的边际（A 上的概率分布）形成。

After all, it's  what we were routinely falling back on in order to check ourselves that the Bayes factor  computation even made sense in the first place.

毕竟，这是我们经常用来检查贝叶斯因子计是否有意义的方法。

I think that's what this notation says, but we don't see so clearly Bayes' theorem.

我认为这就是这个符号所说的，但我们没有那么清楚地看到贝叶斯定理。

And that requires us to talk about Bayes rule and also probability density functions.

而这需要我们讨论贝叶斯规则和概率密度函数。

The reason I don't like using this notation in general, even though I can see Bayes' Theorem, is that bar usually means given.

即使我可以看到贝叶斯定理，我通常不喜欢使用这种表示法的原因是 bar 通常表示定。

In this video you and i will dig into this  paradox, but instead of using it to talk about the usual version of bayes rule, I'd like to motivate  an alternate version, an alternate design choice.

在这段视频中， 你和我将深入探讨这个悖论，但不是用它来讲解通常版本的贝叶斯规则，我想引导出一个替代版本，一个不同的设计选择。

But with the other formula, to do that you have to recompute everything each time, you can't  leverage a pre-computed Bayes factor the same way.

但是用另一个公式， 每次你得重新计所有内容，你不能像那样利用预先计好的贝叶斯因子。

I was reading through a Bayes book that carried through this argument about the prosecutor's fallacy.

我正在阅读一本贝叶斯的书，这本书贯穿了关于检察官谬误的论点。