Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.2K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.2K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

7.6K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
7.6K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.0K
Weighted Mean00:57

Weighted Mean

4.9K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
4.9K
Confidence Coefficient01:24

Confidence Coefficient

7.4K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
7.4K
Outliers and Influential Points01:08

Outliers and Influential Points

3.9K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
3.9K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

CagA-dependent expression of anti-inflammatory cytokine IL-13 and TNFRSF member Fn14 in <i>Helicobacter pylori</i> infected gastric cells and tissues.

Frontiers in microbiology·2026
Same author

Experimental Evaluation and Prediction of the Dynamic Modulus of Crumb Rubber-Modified Stone Mastic Asphalt Mixtures.

Polymers·2026
Same author

Development of oral piperaquine nanoparticles: A study on formulation, characterization and in-vivo performance.

Pakistan journal of pharmaceutical sciences·2026
Same author

Unlocking shale gas potential in the Sembar Formation through multi-analytical assessment and basin modeling, Central Indus Basin, Pakistan.

Scientific reports·2026
Same author

Closing the loop: A systematic review of artificial intelligence in circular e-waste management.

Waste management (New York, N.Y.)·2026
Same author

Molecular docking and pharmacological investigations of folenolide for its analgesic, anti-inflammatory, and antipyretic applications.

Frontiers in pharmacology·2025
Same journal

Therapeutic potential of crude protein extracts from two Egyptian freshwater snails Lanistes carinatus and Bellamya unicolor.

Scientific reports·2026
Same journal

Microbial contamination of donor corneas and post-keratoplasty endophthalmitis: a comparison between Japanese and U.S. eye banks using cold storage.

Scientific reports·2026
Same journal

Prevalence and contributing factors of virological non-suppression among adult patients on first-line antiretroviral therapy in tertiary hospitals in Ethiopia.

Scientific reports·2026
Same journal

An in vitro comparison of color stability between alkasite and different restorative materials in various staining solutions.

Scientific reports·2026
Same journal

Toward accessible mRNA LNP formulation: systematic evaluation of mixing strategies and key parameters.

Scientific reports·2026
Same journal

A network analysis of personality traits, mentalizing, and psychological health in Chinese college students.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: May 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.5K

人群在边缘计算使用权重知识蒸.

Muhammad Asif Khan1, Hamid Menouar2, Ridha Hamila3

  • 1Qatar Mobility Innovations Center, Qatar University, Doha, Qatar. asifk@ieee.org.

Scientific reports
|April 8, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了知识蒸,以增强轻量级人群计数模型,在不牺牲速度的情况下提高资源有限的设备的准确性. 该方法帮助浅层网络从更深层的网络中学习,以便更好地实时进行人群分析.

更多相关视频

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K
Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain
11:25

Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain

Published on: May 14, 2009

13.7K

相关实验视频

Last Updated: May 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.5K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.2K
Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain
11:25

Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain

Published on: May 14, 2009

13.7K

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 视觉人群计数研究已经取得了进展,解决了尺度变化和遮问题.
  • 现有的方法往往优先考虑准确性,而不是模型大小和计算效率.
  • 无人机等资源有限的设备需要轻量级的实时人群计数模型,但这些模型往往缺乏准确性.

研究的目的:

  • 为了解决轻量级人群计数模型的精度下降.
  • 提高浅人群模型的学习能力和概括性.
  • 为了在边缘设备上实现高效的实时人群计数.

主要方法:

  • 建议知识蒸,将知识从更深层次的模型转移到轻量级的模型.
  • 训练轻量级人群模型以模拟更大,更复杂的模型的行为.
  • 在六个基准数据集中,对三个轻量级模型进行了广泛的实验.

主要成果:

  • 在轻量级人群计数模型的准确性方面取得了显著的改进.
  • 验证了知识蒸用于提高浅层网络性能的有效性.
  • 废弃研究证实了拟议的知识蒸方法的贡献.

结论:

  • 知识蒸是一种有效的技术,可以提高轻量级人群计数模型的性能.
  • 拟议的方法可以在具有有限计算资源的设备上实现准确的实时人群计数.
  • 这项研究有助于开发用于现实世界的应用程序的实用人群分析系统.