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

Uncertainty: Overview00:59

Uncertainty: Overview

550
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
550
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

680
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
680
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.1K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

516
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
516
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

73.6K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
73.6K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

5.7K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
5.7K

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

A PbS quantum dot film as a hole transport layer for self-powered AgBiS<sub>2</sub> nanocrystal photodetectors.

Chemical communications (Cambridge, England)·2026
Same author

Artificial intelligence for detecting fetal orofacial clefts and advancing medical education.

Nature communications·2026
Same author

HER2∆16 directs luminal cell identity and estrogen receptor signaling in HER2+ breast cancer.

Nature communications·2026
Same author

Tirofiban for Reduction of TEAR: A Phase 2, Randomized, Open-Label, Blinded End Point, Controlled Trial.

Stroke·2026
Same author

Epigenetic reprogramming of tissue-resident memory T cells in chronic inflammatory disorders and implications for targeted therapies.

Epigenomics·2026
Same author

Photoinduced Cyclization of 2-Alkynylanilines to Access 3-Bromoindole Scaffolds.

The Journal of organic chemistry·2026
Same journal

Inner layer security reinforcement for instant payment systems: a dual layer encryption-steganography evaluation in Brunei's digital payment context.

Frontiers in big data·2026
Same journal

Measuring the impact of virtualization and containerization on the environment when using GPUs for processing the AI models.

Frontiers in big data·2026
Same journal

Using artificial intelligence to improve governance and public services in Africa.

Frontiers in big data·2026
Same journal

Case count metric for comparative analysis of entity resolution results.

Frontiers in big data·2026
Same journal

Data field theory: a geometric framework for learning on Riemannian manifolds with synthetic validation and limitation analysis.

Frontiers in big data·2026
Same journal

Correction: Explainable gradient convolutional vector fuzzy pattern analysis based on ensemble model for facial expression recognition.

Frontiers in big data·2026
查看所有相关文章

相关实验视频

Updated: Jun 25, 2025

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

10.3K

在图形神经网络中的量化不确定性解释.

Junji Jiang1, Chen Ling2, Hongyi Li3

  • 1School of Management, Fudan University, Shanghai, China.

Frontiers in big data
|May 24, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一个新的框架来量化图形神经网络 (GNN) 解释中的不确定性. 它解决了图形数据和模型参数中的随机性,以实现更可靠的GNN预测.

关键词:
深度学习是一种深度学习.解释不确定性解释不确定性图表神经网络的神经网络不确定性量化不确定性量化变化机制的变化机制

更多相关视频

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K

相关实验视频

Last Updated: Jun 25, 2025

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

10.3K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K

科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 图形神经网络的神经网络

背景情况:

  • 图形神经网络 (GNN) 越来越多地用于复杂的数据分析.
  • 现有的GNN解释方法经常忽视数据和模型参数中的不确定性,导致不可靠的解释.
  • 在后 hoc,模型不可知的 GNN 解释中量化不确定性是具有挑战性的.

研究的目的:

  • 开发一个新的框架,用于在GNN解释中量化不确定性.
  • 通过考虑数据和参数不确定性来解决现有方法的局限性.
  • 提高GNN预测的可靠性和可信度.

主要方法:

  • 为GNN解释提出了一个新的不确定性量化框架.
  • 该框架考虑了两种不同的数据不确定性,以评估解释不确定性.
  • 它直接从数据中学习参数分布,在没有分布假设的情况下量化解释不确定性.

主要成果:

  • 拟议的框架成功地量化了来自图形数据和模型参数的不确定性.
  • 它与现有的GNN后期解释方法无集成.
  • 经验结果表明,在现实世界的基准标准上,GNN解释性能优越.

结论:

  • 新的框架为GNN解释中的不确定性量化提供了一个强大的解决方案.
  • 这种方法通过考虑固有的不确定性来提高GNN预测的可靠性.
  • 该方法为GNN解释性能和可靠性设定了新的标准.