Chebyshev inequality

Chebyshev inequality的基本解释

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切比雪夫不等式

与Chebyshev inequality相关的网络例句 [注：此内容来源于网络，仅供参考]

Markov's inequality and Chebyshev's inequality are very important in probabilistic theory.

Markov不等式和Chebyshev不等式是概率论中两个重要的不等式。

The famous Chebyshev's Inequality is usually used when estimating the boundary from above or below of probability .

在估计某些事件的概率的上下界时，常用到著名的切比雪夫不等式。

Chebyshev's Inequality and Law of Large Numbers are the foundational work on the limit theory of probability.

其创立的切贝舍夫不等式和切贝舍夫大数定律是概率论极限理论的基础，从而使概率论成为严密数学分支。

While Chebyshev's Inequality gives an upper bound on P, there is no guarantee that the upper bound is a good approximation.

因为切尔雪夫不等式给出的是每个象素灰度的上限，而又没法保证其上限是一个很好的估计。

We then apply the one-tailed version of Chebyshev's Inequality to estimate the percentage of light reaching a surface at depth t.

这样我们应用一个修改版的切尔雪夫不等式去估计光源到达深度t平面的百分比。

与Chebyshev inequality相关的网络解释 [注：此内容来源于网络，仅供参考]

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