通常你要分配的每个实例化代表着不相关的随机变量，这就意味着你应该为每一个新的引擎。
Usually you want every instantiation of a distribution to represent an uncorrelated stochastic variable, which means that you should a new engine for each one.
本文证明了由两随机变量的独立性可推出它们的不相关性，但逆命题不成立。
In this paper, it is proved that two random variables' independence can infer their no-correlation and its untenable inverse proposition.
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