WLLN:弱大数定律
弱大数定律(英文全称“Weak Law of Large Numbers”,常缩写为WLLN),是概率论与数理统计中的一项基本定理,广泛应用于数学、统计学及相关科研领域。采用缩写WLLN既便于书写,也有助于在学术论文、教材和专业交流中提高表达效率。该定律描述了在大量独立重复试验中,样本均值依概率收敛于期望值的统计规律,是理解大样本理论的重要基础。
Weak Law of Large Numbers具体释义
Weak Law of Large Numbers的英文发音
例句
- A weak law of large numbers for the weighted sums of non-identically distributed NA random matrix sequences is studied.
- 研究了不同分布NA序列加权和最大值的弱大数定律(WLLN),推广了前人的结果。
- This note is devoted to introduce the concept of dominated random sequence and give a weak law of large numbers for dependent random sequence.
- 引入受控随机序列的概念,给出了独立随机序列的一个弱大数定律(WLLN)。
- We extend the large deviation principle by proving the local uniform lower bound. We also give a new variational formula for the principal eigenvalue and a strong version of weak law of large numbers.
- 然后我们证明了局部一致大偏差下界,给出了新的主特征值变分公式和加强形式的弱大数定律(WLLN)。
- The Weak Law of Large Numbers(WLLN) for Weighted Sums of Random Variable Sequences of Independent and Identical Distribution
- 独立同分布随机变量列加权和的弱大数定律(WLLN)
- The Euler's Weak Law of Large Numbers(WLLN) in Banach Space
- Banach空间中的Euler弱大数定律(WLLN)
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