uniform distribution

简明释义

均匀分布

英英释义

例句

1.The time taken for customers to arrive at a store can be modeled with a uniform distribution 均匀分布 if arrivals are evenly spaced throughout the day.

如果顾客到达商店的时间在一天内均匀分布，可以用均匀分布 均匀分布来建模。

2.The random selection of lottery numbers can be modeled as a uniform distribution 均匀分布 since each number has the same probability of being chosen.

彩票号码的随机选择可以建模为均匀分布 均匀分布，因为每个号码被选中的概率相同。

3.In a fair dice game, the outcome of rolling a die follows a uniform distribution 均匀分布, where each number has an equal chance of appearing.

在一个公平的掷骰子游戏中，掷骰子的结果遵循均匀分布 均匀分布，每个数字出现的机会相等。

4.In quality control, if the dimensions of a product are uniformly distributed, we can expect most measurements to fall within a certain range due to uniform distribution 均匀分布.

在质量控制中，如果产品的尺寸是均匀分布的，我们可以预期大多数测量值会由于均匀分布 均匀分布而落在某个范围内。

5.When generating random numbers for simulations, a uniform distribution 均匀分布 ensures that all values in a specified range are equally likely.

在生成随机数进行模拟时，均匀分布 均匀分布确保指定范围内的所有值都有相同的可能性。

作文

In the realm of statistics and probability theory, the concept of uniform distribution is fundamental. It describes a situation where all outcomes are equally likely to occur. This means that if you were to conduct an experiment or a survey, each possible outcome would have the same chance of being selected. For instance, consider rolling a fair six-sided die. Each number from one to six has an equal probability of 1/6. This scenario exemplifies a discrete uniform distribution. On the other hand, in a continuous context, a uniform distribution can be represented by a range of values. Imagine a scenario where we are measuring the height of individuals in a population that ranges from 150 cm to 200 cm. If every height within this range has an equal chance of occurring, we can say that the heights follow a continuous uniform distribution. Understanding uniform distribution is crucial for various applications in fields such as computer science, engineering, and social sciences. For example, in computer simulations, when random numbers are generated, they often need to follow a uniform distribution to ensure fairness and randomness. This allows programmers to create algorithms that simulate real-world scenarios accurately. Moreover, the uniform distribution serves as a baseline for comparing other distributions. For instance, when analyzing data, if we find that a dataset does not follow a uniform distribution, it might indicate that certain factors are influencing the outcomes more than others. This could lead researchers to explore underlying causes or biases within the data. Additionally, the concept of uniform distribution is essential in decision-making processes. For example, businesses often use statistical models to forecast sales or customer behavior. If they assume that customer preferences are evenly distributed, they might apply a uniform distribution model to predict trends. However, if actual customer behavior reveals a different pattern, businesses may need to adjust their strategies accordingly. In conclusion, the uniform distribution is a foundational concept in statistics that illustrates how outcomes can be evenly spread across a given range. Its applications are vast, impacting various fields from scientific research to everyday decision-making. By understanding the principles behind the uniform distribution, individuals and organizations can make more informed decisions based on statistical analysis. As we continue to collect and analyze data, recognizing when a uniform distribution applies can lead to deeper insights and better outcomes in our endeavors.

在统计学和概率论的领域中，均匀分布的概念是基础性的。它描述了一种所有结果发生的可能性相等的情况。这意味着如果你进行实验或调查，每个可能的结果都有相同的被选择的机会。例如，考虑掷一个公平的六面骰子。从一到六的每个数字都有相等的概率为1/6。这种情况就是一个离散的均匀分布。另一方面，在连续的上下文中，均匀分布可以通过一系列值来表示。想象一下，我们正在测量一个人口的身高，该人口的身高范围从150厘米到200厘米。如果这个范围内的每个身高都有相等的发生机会，我们可以说这些身高遵循一个连续的均匀分布。理解均匀分布对于计算机科学、工程学和社会科学等多个领域的各种应用至关重要。例如，在计算机模拟中，当生成随机数时，它们通常需要遵循均匀分布以确保公平性和随机性。这使程序员能够创建准确模拟现实场景的算法。此外，均匀分布作为比较其他分布的基准。例如，在分析数据时，如果我们发现一个数据集不遵循均匀分布，这可能表明某些因素对结果的影响大于其他因素。这可能导致研究人员探讨数据中的潜在原因或偏见。另外，均匀分布的概念在决策过程中也至关重要。例如，企业通常使用统计模型来预测销售或客户行为。如果他们假设客户偏好是均匀分布的，他们可能会应用均匀分布模型来预测趋势。然而，如果实际客户行为揭示了不同的模式，企业可能需要相应地调整他们的策略。总之，均匀分布是统计学中的一个基础概念，它说明了结果如何在给定范围内均匀分布。它的应用广泛，影响着从科学研究到日常决策的各个领域。通过理解均匀分布背后的原理，个人和组织可以基于统计分析做出更明智的决策。随着我们继续收集和分析数据，识别何时适用均匀分布可以带来更深刻的洞察和更好的成果。