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遇到这种情况,有些人就会说上面这个概率的概念在这里并不适用。
In cases like this some people would say that the notion of probability does not apply.
在电池容量不足够的情况下,要想定位器能待机5年,这个概率很小。
If the battery capacity is not enough, the probability that the tracking equipment lasts 5 years is very small.
如果此时第三帧显示物体在屏幕的中间,那么这个概率还会继续增加,以此类推。
If a third frame shows the object in the middle of the screen, that probability increases even more, and so on.
OK,现在让我们来想想如何构建这个问题的依赖性。在这个概率分布中,让我们.
Okay, so now let's think about how we can construct the dependencies of this in this probability distribution.
有一个问题,但是我们的训练集中并没有出现“Averyclosegame”,所以这个概率是零。
There's a problem though:“A very close game” doesn't appear in our training data, so this probability is zero.
施姆兰的论文解释了为什么这个概率如此接近、但却不是18{\displaystyle{\frac{1}{8}}}。
Schmuland's paper explains why this probability is so close to, but not exactly, 1/8.
在世界各地,惨烈车祸中的死亡率为1/6,这个概率与死于某些常见的健康问题的概率相当,如心脏病。
Around the world, deaths in a fatal car crashes are 1/6th as likely as dying from a common health problem, like heart disease.
这个概率就是P值。
This probability is the p value.
这个概率是2.4%。
This probability is 2.7.
有什么办法提高这个概率??
What can you do to increase that probability?
问题是很难计算这个概率
The problem is that it is difficult to estimate this probability.
这个概率叫做置信水平。
That likelihood is called the confidence level.
我们将这个概率称之为p-value。
We call this probability the“p-value.
我们将这个概率称之为p-value。
This probability is called p-value.
我们将这个概率称之为p-value。
That probability is called the p-value.
我们将这个概率称之为p-value。
This probability is known as the p-value.
布丰本人证明了,这个概率是.
Boltzmann principle, this probability is.
在加利福尼亚,这个概率是大约6%。
In California, that probability is about 6.
这个概率被称为置信水平。
That likelihood is called the confidence level.
这个概率分布是独立于初始分布的。
This probability distribution is independent of the initial distribution.