在证明中采用了一种把网微分法与条件矩母函数相结合应用于随机选择系统强极限定理研究的一种途径。
In the proof, the tools of the conditional moment generating function and the differentiation on a net for the study on strong limit theorems in the random selection system are applied.
在广义帕累托分布模型中，门限值过小，极限定理不成立，得到的估计是有偏的；
In GPD model, a too low threshold will get biased estimates because the limit theorem does not apply.
这就是概率论中第二个基本极限定理的原始初形。
This is the probability limit theorems in the second basic form of the original first.
最后，我们介绍了风险理论中的极限定理。
而对几乎处处极限定理和自赋范极限理论的研究则是近几十年来概率极限理论研究中的两个重要方向。
The almost sure central limit theorem and self-normalized limit theory have become two important fields of the study of probability limit theory in recent decades.
证明中应用了研究马尔可夫链强极限定理的一种新的分析方法。
In the proof a new analytic technique in the study of the strong limit theorems for Markov chains is applied.
本文讨论中心极限定理在抽样推断中的若干应用。
Some applications of the central limit theorem in sampling deduction were discussed in this paper.
摘要指出并分析了目前国内概率论教材中中心极限定理部分存在的一个问题。
A problem in teaching of central limit theorem is discussed in this paper.
主要研究任意随机序列在随机选择系统中的随机条件概率其调和平均的强极限定理。
The strong limit theorems on the arbitrary stochastic convergence for the harmonic mean of the random conditional probabilities in the random selection system is studied.
定义中心极限定理，并且理解其在置信区间、控制图等统计推断应用中的意义。
Define the central limit theorem and understand its significance in the application of inferential statistics for confidence intervals, control charts, etc.
第一章，主要介绍了风险模型和风险理论中的极限定理。
Chapter 1 is mainly about the discrete risk model and its limit theory.
首先在引理中得出了隐非齐次马尔可夫模型的一些性质，从而导出了隐非齐次马尔可夫模型的三元函数一类平均值的强极限定理。
At first some properties on nonhomogeneous Markov models are obtained in the lemma, then a limit theorem for the average of the three variables function of hidden nonhomogeneous Markov model is given.
首先在引理中得出了隐非齐次马尔可夫模型的一些性质，从而导出了隐非齐次马尔可夫模型的三元函数一类平均值的强极限定理。
At first some properties on nonhomogeneous Markov models are obtained in the lemma, then a limit theorem for the average of the three variables function of hidden nonhomogeneous Markov model is given.
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