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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

11.5K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Sampling Plans01:23

Sampling Plans

155
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
155
Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Sampling Distribution01:12

Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
11.7K
Sampling Methods: Overview01:06

Sampling Methods: Overview

225
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
225
Stratified Sampling Method01:16

Stratified Sampling Method

11.6K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Updated: May 9, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
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基于成本意识的来源采样之间的间隔性进行了通用的网络拆解.

Jihui Han1, Chengyi Zhang1, Gaogao Dong2

  • 1School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou 450000, Henan, China.

Chaos (Woodbury, N.Y.)
|May 2, 2025
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概括
此摘要是机器生成的。

我们开发了一种新的算法,即成本意识源采样间隔 (CASS-Bet),用于高效的网络拆解. 这种方法平衡了节点的重要性和移除成本,在基础设施保护和犯罪控制方面表现优越.

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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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科学领域:

  • 网络科学 网络科学
  • 计算机科学 计算机科学
  • 应用数学 应用数学 应用数学

背景情况:

  • 网络拆解对于基础设施保护和流行病控制等关键应用至关重要.
  • 当前的方法经常在现实场景中与效率和成本意识作斗争.

研究的目的:

  • 引入一种新的,可扩展的算法,用于成本意识的网络拆解.
  • 为了动态平衡节点重要性和移除成本,以优化网络中断.

主要方法:

  • 开发了成本意识源采样间隔 (CASS-Bet) 算法.
  • 基于实时网络变化实现了动态节点优先级.
  • 利用可扩展的采样技术来提高计算效率.

主要成果:

  • 在社会,基础设施和犯罪网络中,CASS-Bet表现出卓越的表现.
  • 该算法使得成本效益高的网络拆解能够在最小的资源开支下实现.
  • 在定义移除成本方面实现了高计算效率和灵活性.

结论:

  • 对于现实世界的网络拆解挑战,CASS-Bet提供了一种实用且可扩展的解决方案.
  • 该算法增强了基础设施的弹性,并有助于破坏有组织犯罪网络.
  • 提供了一个灵活的框架,可以适应各种实际成本限制.