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

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

445
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...
445
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

611
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...
611
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

3.0K
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...
3.0K
Uncertainty: Overview00:59

Uncertainty: Overview

488
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.
488
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

73.0K
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. 
73.0K
Random Error01:04

Random Error

785
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
785

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

Deep Lidar-Guided Image Deblurring.

Sensors (Basel, Switzerland)·2025
Same author

Compressive Sensing Imaging Spectrometer for UV-Vis Stellar Spectroscopy: Instrumental Concept and Performance Analysis.

Sensors (Basel, Switzerland)·2023
Same author

RAN-GNNs: Breaking the Capacity Limits of Graph Neural Networks.

IEEE transactions on neural networks and learning systems·2021
Same author

Deep Graph-Convolutional Image Denoising.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2020
Same author

Improved iterative shrinkage-thresholding for sparse signal recovery via Laplace mixtures models.

EURASIP journal on advances in signal processing·2019
Same author

Sparsity estimation from compressive projections via sparse random matrices.

EURASIP journal on advances in signal processing·2019
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026
Same journal

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

IEEE transactions on neural networks and learning systems·2026
Same journal

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: May 15, 2025

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

7.8K

对于高斯斯裂变的建模不确定性

Luca Savant Aira, Diego Valsesia, Enrico Magli

    IEEE transactions on neural networks and learning systems
    |April 8, 2025
    PubMed
    概括
    此摘要是机器生成的。

    随机高斯分辨率 (SGS) 引入高斯分辨率 (GS) 的不确定性估计,改进了新视图合成. 这一框架提高了生成图像的可靠性和准确性,有助于现实世界的应用.

    更多相关视频

    Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
    10:22

    Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

    Published on: September 7, 2019

    8.2K
    Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence
    12:34

    Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence

    Published on: June 24, 2016

    10.0K

    相关实验视频

    Last Updated: May 15, 2025

    Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
    06:55

    Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

    Published on: September 26, 2016

    7.8K
    Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
    10:22

    Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

    Published on: September 7, 2019

    8.2K
    Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence
    12:34

    Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence

    Published on: June 24, 2016

    10.0K

    科学领域:

    • 计算机视觉 计算机视觉
    • 计算机图形 计算机图形
    • 机器学习 机器学习

    背景情况:

    • 高斯斯 (GS) 提供高效,高品质的新视角合成.
    • 目前的GS方法缺乏对合成视图的不确定性量化.
    • 神经辐射场 (NeRFs) 提供了信心测量,但在计算上是昂贵的.

    研究的目的:

    • 开发第一个框架,用于高斯斯裂变 (GS) 中的不确定性估计.
    • 将不确定性预测集成到GS染管道中.
    • 提高新视角合成的可靠性和准确性.

    主要方法:

    • 介绍了基于变化推理 (VI) 的方法 - - 随机高斯分裂 (SGS).
    • 将不确定性预测集成到标准GS染管道中.
    • 将分散错误下的面积 (AUSE) 纳入损失函数以进行关节优化.

    主要成果:

    • 在三个数据集上,SGS在现有方法上表现出优越的性能.
    • 在图像染质量和不确定性估计准确性方面取得了最先进的结果.
    • 提供了对综合观点信心的可靠见解.

    结论:

    • 斯格斯有效地量化了高斯斯裂纹的不确定性.
    • 该框架提高了新视角合成的可信度.
    • 在使用合成图像的现实应用程序中实现更安全的决策.