在 中文 中使用 一个概率 的示例及其翻译为 英语
{-}
-
Political
-
Ecclesiastic
-
Programming
P（A）和P（B）是一个概率和b彼此独立地（边际概率）的发生。
P(A) and P(B) are the probabilities of A and B occurring independently of one another(the marginal 积极且慎重地去限制一个概率空间的练习效用远远超出了科学、数学和游戏设计。
The exercise of actively and deliberately limiting a possibility space has utility well beyond science, mathematics, and game design.每个事件都有一个概率，这取决于系统的状态，也可能取决于其以前的历史。
Each event has a probability that depends on the state of the system and perhaps also on its previous history.从本质上讲，你的模型实际上是一个概率表，通过你的训练数据得到更新。
Basically, your model is really a likelihood table that gets updated through your training data.它们只给我们一个概率，是不透明的，难以解释。
They just give us a probability, and they are opaque and hard to interpret.你的先验概率通常会包含一个概率的范围，而不是一个单一的数值。
Your prior will often consist 从本质上讲，你的模型实际上是一个概率表，通过你的训练数据得到更新。
Essentially, your model is actually a probability table that gets updated through your training data.例如，他们评估了发生各种机械故障的原因，并为每一种可能发生的机械故障设定一个概率。
For instance, they assessed the likeliness of various mechanical failures, and came up with a probability for each scenario.对于每个我们在训练样本中遇到的词，我们有一个概率去删除它。
For each word we encounter in our training text, there is a chance that we will effectively delete it from the text.注意，原则上，因为他们允许创建一个概率模型，loss=”log”和loss=”modified_huber”更合适one-vs-all分类。
Note that, in principle, since they allow to create a probability model, loss="log" and loss="modified_huber" are more suitable for one-vs-all classification.使用loss=”log”或loss=”modified_huber”可以启用predict_proba方法，它给出了一个概率估计向量，每个样本x都有对应一个P(yx)：.
Using loss="log" or loss="modified_huber" enables the predict_proba method, which gives a vector of probability estimates\(P(y|x)\) per sample\(x\).设独立随机向量(Y1,…,YJ){\displaystyle(Y_{1},\ldots，Y_{J})}，每个都有一个概率质量函数：.
Suppose a vector of independent random variables( Y 1,…, Y J){\displaystyle(Y_{1},\ldots,Y_{J})} with each Y i{\displaystyle Y_{i}} having a probability mass function as.给定一个概率空间.
Given a probability space.给定一个概率空间.
Generating a Probability Space.投资是一个概率游戏。
Investment is a probability game.贝叶斯定理是一个概率计算公式：.
The Bayesian formula is a conditional probability calculation scheme.每个预测都可看作是一个概率或预期值。
Each prediction is interpretable as a probability or 也就是说,系综是系统状态的一个概率分布。
In other words, 设（Ω,F,P）为一个概率空間。
Let( Ω, F, P) be a complete probability space.然后我假设有一个世界w，这也是一个概率空间。
Then I posited 
