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 Random Error00:59

Propagation of Uncertainty from Random Error

681
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...
681
Uncertainty: Overview00:59

Uncertainty: Overview

552
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.
552
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

517
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...
517
Contaminants and Errors01:16

Contaminants and Errors

89
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
89
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

73.7K
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. 
73.7K

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

Dismantling the TGF-β Axis: A Critical Transition from Occupancy-based Kinase Inhibitors to Event-driven Degraders.

Mini reviews in medicinal chemistry·2026
Same author

Simultaneous inhibition of Microcystis growth, toxin production, and release by a high-efficacy algicidal bacterium Aeromonas sp. N3.

Journal of applied microbiology·2026
Same author

SARS-CoV-2 ORF3a suppresses host antiviral interferon responses by promoting STUB1-mediated PTEN proteasomal degradation.

Journal of virology·2026
Same author

Extracting Genetically-Imputed Causal Features From ECG Data.

Statistical analysis and data mining·2026
Same author

Zn-dihydromyricetin/Cu-dual-network hydrogel antimicrobial coating for cascade repair of bone defects.

Biomaterials advances·2026
Same author

Silk fibroin-loaded Fe-curcumin nanoparticles on antimicrobial peptide-functionalized TiO<sub>2</sub> nanotube surfaces: Microenvironment-modulated synergy for antibacterial and osteogenic enhancement.

Biomaterials advances·2026

相关实验视频

Updated: Jun 28, 2025

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

6.2K

通过扰动辅助样本合成进行新型不确定性量化.

Yifei Liu, Rex Shen, Xiaotong Shen

    IEEE transactions on pattern analysis and machine intelligence
    |April 24, 2024
    PubMed
    概括

    本研究提出了使用合成数据量化不确定性的新框架. 扰动辅助推理 (PAI) 框架增强了数据多样性和隐私,提高了深度学习模型的可靠性.

    科学领域:

    • 人工智能的人工智能
    • 统计 统计 统计 统计
    • 数据科学数据科学数据科学

    背景情况:

    • 不确定性量化对于在复杂数据场景中可靠的决策至关重要.
    • 深度学习模型经常在不确定性估计方面扎,尤其是在非结构化数据方面.
    • 现有的方法可能缺乏统计保障,或需要大量的数据.

    研究的目的:

    • 引入一个新的框架,扰动辅助推理 (PAI),用于稳健的不确定性量化.
    • 通过扰乱辅助样本合成 (PASS) 利用合成数据生成,以提高数据多样性和隐私.
    • 确保统计上有保证的推理有效性,即使没有先前的分布知识.

    主要方法:

    • PASS使用具有数据扰动的生成模型来创建保留排名属性的合成数据.
    • 从预先训练的生成模型转移知识可以改善分布式估计.
    • PAI确保了关键推断的有效性,并在非关键情况下使用持久样本来确保可靠性.

    主要成果:

    • 通过生成多样化,保护隐私的合成数据,提高估计准确度.
    • PAI提供了统计上有保证的推断,在没有先前的分布知识的情况下改进结论.
    • 该框架在各种应用中显示出有效性,包括图像合成和多式联络推理.

    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
    NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode
    09:19

    NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

    Published on: June 4, 2021

    3.3K

    相关实验视频

    Last Updated: Jun 28, 2025

    Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
    05:51

    Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

    Published on: July 19, 2019

    6.2K
    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
    NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode
    09:19

    NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

    Published on: June 4, 2021

    3.3K

    结论:

    • PAI框架为复杂数据环境中的不确定性量化提供了一个统计学上合理的方法.
    • 通过有效地生成高质量的合成数据,增强数据多样性和隐私.
    • 在推进需要可靠的不确定性估计的数据驱动任务方面,PAI显示了广泛的适用性和有效性.