对程序员来说，最有效的离散数学的分支是概率理论。这是你在学校学完基本算术后的紧接着的课。你会问，什么是概率理论呢？
For programmers, the most useful branch of discrete math is probability theory. It's the first thing they should teach you after arithmetic, in grade school.
保险机构的出现，我认为这是金融业中，最早的分支之一，大约在17世纪，概率论刚刚诞生。
The institution of insurance is something that really came in — it's one of the earliest — I consider it a division of finance — really came in the 1600s when probability theory was invented.
数理统计(也称为统计理论)是应用数学分支，利用概率论和分析研究的理论基础的统计数据。
Mathematical statistics (also called statistical theory) is the branch of applied mathematics that USES probability theory and analysis to examine the theoretical basis of statistics.
信息理论作为应用概率论的一个分支，应用日益重要。
Information theory, as a branch of applied probability theory, becomes more and more important in appling.
此外，还有一个分支统计要求准确的统计数字，是根据确切概率报表。
Moreover, there is a branch of statistics called exact statistics that is based on exact probability statements.
对程序员来说，最有效的离散数学的分支是概率理论。
For programmers, the most useful branch of discrete math is probability theory.
同时还可以为抽象的数学问题提供具体的概率背景，沟通各数学分支之间的联系。
Meanwhile, this can still provide a concrete probability background for abstract mathematics questions and build up some communicating between every mathematics branch.
概率极限理论是概率论的主要分支之一，也是概率论的其他分支和数理统计的重要基础。
Limit theory is one of the key branches of Probability theory, and also the important foundation of other branches.
概率理论是数学的一个分支，它利用数字1或0来表示一个已知事件是否发生，并且计算其可能性。
Probability theory is a branch of mathematics that deals with calculating the likelihood of a given event's occurrence, which is expressed as a number between 1 and 0.
随机分形是融概率论、经典分析和几何学于一体的新兴数学分支。
Random fractal, which involves probability, classical analysis and geometry, is a new mathematics branch.
概率论是数学的一个重要分支，不等式的证法是多种多样的。
The probability is one of the most important branches of mathematics There are various methods of proving the inequality.
先验概率值大于0.8的标在了分支节点处。
Values of Bayesian posterior probability greater than 0.8 are shown at the nodes.
先验概率值大于0.8的标在了分支节点处。
Values of Bayesian posterior probability greater than 0.8 are shown at the nodes.
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