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相关概念视频

Random Variables01:09

Random Variables

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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
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Randomized Experiments01:13

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Graphs of Equations in Two Variables01:30

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An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
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Vector Algebra: Graphical Method01:10

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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Sequence Networks of Rotating Machines01:24

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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一组主变量用于双星随机图.

Pawat Akara-Pipattana1, Oleg Evnin2,3

  • 1Université Paris-Saclay, CNRS, LPTMS, 91405 Orsay, France.

Entropy (Basel, Switzerland)
|October 28, 2025
PubMed
概括

我们为大型随机图引入主变量,简化了分析. 这种方法精确地计算了密集和稀疏图表模式中的现有模型的校正.

科学领域:

  • 统计物理学的统计物理.
  • 网络科学 网络科学
  • 图形理论是指图形的理论.

背景情况:

  • 指数式随机图模型对于网络分析至关重要.
  • 两星模型是最简单的边缘交互模型.
  • 分析大型网络 (N → ∞) 需要强大的理论框架.

研究的目的:

  • 开发一种用于分析大型指数随机图的新方法.
  • 引入辅助"主变量"来控制热力学极限.
  • 在图形理论中提供对1/N校正的明确控制.

主要方法:

  • 引入辅助"主变量"来管理热力学极限 (N → ∞).
  • 应用这些变量来分析密集和稀疏的图形模式.
  • 在没有函数积分的情况下,对平均场解决方案的校正的导出.

主要成果:

  • 该方法恢复了Park-Newman平均场解决方案,用于具有显式1/N校正控制的密度图.
  • 计算了Park-Newman结果的第一个子负载校正,量化了不广泛的自由能量贡献.
  • 实现了对稀疏图的Annibale-Courtney解决方案的紧衍生,绕过了复杂的功能积分方法.
关键词:
指数式随机图形模型的模型.大N的方法.统计学领域理论的统计学领域理论.

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结论:

  • 提出的"主变量"方法为大N的随机图分析提供了一个强大而简单的框架.
  • 这种方法提供了对纠正的明确控制,提高了理论预测的准确性.
  • 它为研究稀疏和密集的随机图模型的现有技术提供了更容易获得的替代方案.