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相关概念视频

Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Bar Graph01:07

Bar Graph

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Relative Frequency Histogram01:14

Relative Frequency Histogram

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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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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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Types of Skewness01:09

Types of Skewness

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If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However,...
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Signal Flow Graphs01:18

Signal Flow Graphs

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Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
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相关实验视频

Updated: Jun 9, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
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Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

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股票市场的功能超图.

Jerry Jones David1, Narayan G Sabhahit2, Sebastiano Stramaglia3

  • 1Complex Systems Lab, Department of Physics, Indian Institute of Technology Indore, Khandwa Road, Indore 453552, India.

Entropy (Basel, Switzerland)
|October 25, 2024
PubMed
概括

本研究引入了功能超图来建模复杂的股票市场相互作用,超出对对相关性. 这种更高阶的方法揭示了股票市场崩盘期间的市场动态和稳定性.

关键词:
复杂的系统复杂的系统.超图是指一个超图.股票市场 股票市场 股票市场

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科学领域:

  • 量化金融 量化金融
  • 网络科学 网络科学
  • 信息理论 信息理论

背景情况:

  • 股票市场价格表现出复杂的,非线性相互依赖.
  • 网络分析一直被用来研究股票市场的行为,但通常假设对对相关性.
  • 现实世界的市场互动可以是更高层次的,同时涉及两个以上的实体.

研究的目的:

  • 开发一种新的方法来代表股票市场数据中的更高阶相互作用.
  • 引入功能超图作为分析这些复杂关系的框架.
  • 为了比较超图的分析能力与传统网络在理解市场动态.

主要方法:

  • 利用信息理论工具,从股票市场数据中构建功能超图.
  • 应用高级网络分析技术.
  • 计算和比较功能超图量 (福曼-里奇曲率,·诺伊曼,自向量中心性) 与传统的网络指标.
  • 分析了网络和超图结构随着时间的推移而发生的演变,特别是围绕市场事件.

主要成果:

  • 与对联网络相比,功能超图提供了更丰富的股票市场相互依赖的表现.
  • 对超图数量的分析揭示了与市场事件相关的独特模式和信号.
  • 超图框架在分析市场行为方面表现出强大的稳定性,即使是在股票市场崩盘期间.

结论:

  • 高级表示,如功能超图,对于全面了解股票市场动态至关重要.
  • 这种方法为市场行为和性提供了新的见解.
  • 该方法提供了一个强大的工具,用于分析金融市场以外的复杂系统.