The superyacht known as the Bayesian had a crew of 10 people and 12 passengers.

这艘名为贝叶斯号的超级游艇上有 10 名船员和 12 名乘客。

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

它将带你从一所到能够完全应用贝叶斯定理或多元线性回归。

You'll learn cutting edge mathematics like information theory, Bayesian networks, and causal inference without calculations getting in the way.

你将学习前沿数学，如信息论、贝叶斯网络和因果推理，而需计算。

Which takes us to the other forgotten terms in Bayesian inference.

这就把我们带到了贝叶斯推理他被遗忘的术语。

The Bayesian Award goes to... Wait, wait, wait.

贝叶斯奖颁给......等等，等等，等等。

You understand the heart of Bayes' theorem.

您了解贝叶斯定理的核心。

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

这需要我们讨论贝叶斯规则和概率密度函数。

One of whom basic inference takes its name from, that's Thomas Bayes.

基本推理的名字就来源一位 托马斯·贝叶斯。

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

对这种情况，我们再次有贝叶斯规则。

A common technique for figuring this out is Bayesian knowledge tracing.

贝叶斯识追踪常用来解决这个问题。

So not quite yet, but that computation explains this explosion in the usage of Bayesian statistics.

所以现在还不完全是，但这种计算解释了贝叶斯统计使用的爆炸式增长。

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。

This, my friends, is Bayes' theorem.

我的朋友们，这就是贝叶斯定理。

And this is a major point of confusion for most students that first take a Bayesian class.

对大多数第一次参加贝叶斯课程的学生来说，这是一个主要的困惑点。

It's very commonly used to introduce the topic of bayes rule in  probability.

它非常常用介绍概率的贝叶斯规则主题。

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，这个值还有一个特殊的名字，那就是“可能性”。

Our goal is to estimate P in a Bayesian fashion.

我们的目标是以贝叶斯的方式估计 P。

So the Bayesian says don't even add a full sample to each side.

因此, 贝叶斯模型说, 甚至不要在每边都添加一个完整的样本。

Similarly, Bayesian knowledge tracing updates its estimate of the students’ knowledge by observing the correctness of each interaction using that skill.

同样，贝叶斯识追踪通过观察使用该技能的每次交互的正确性来更新对学生识的估计。

So basic Bayesian constructions, how to form likelihood priors, what are the priors, which ones do we use and why.

那么基本的贝叶斯结构， 如何形成似然先验，什么是先验， 我们使用哪些先验以及为什么。