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相关概念视频

Uncertainty: Overview00:59

Uncertainty: Overview

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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.
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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

Uncertainty in Measurement: Accuracy and Precision

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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. 
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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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...
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Uncertainty in Measurement: Reading Instruments02:46

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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相关实验视频

Updated: Jul 20, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

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一个用于量化规模不确定性的一般框架.

Ionuţ-Gabriel Farcaş1, Gabriele Merlo1, Frank Jenko1,2,3

  • 1Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX USA.

Communications engineering
|July 31, 2023
PubMed
概括
此摘要是机器生成的。

一种新的适应性稀疏网格插值方法使复杂的计算模型能够有效量化不确定性和灵敏度分析. 这种方法显著降低了计算成本,使得大规模分析在科学研究中成为可能.

关键词:
计算科学是一种计算科学.磁性封闭的等离子体

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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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科学领域:

  • 计算科学是一种计算科学.
  • 血物理学的等离子体物理学
  • 核聚变能源的研究.

背景情况:

  • 复杂的计算模型在科学中很普遍,但需要大量的计算资源.
  • 有限的计算能力限制了不确定性量化和灵敏度分析.
  • 在磁性封闭聚变装置中分析流传输是计算密集的.

研究的目的:

  • 引入一种新的灵敏度驱动的尺寸适应性稀疏网格插值策略.
  • 为了使高效和准确的不确定性量化和敏感性分析计算昂贵的模型.
  • 为了证明该方法在聚变研究中的有效性.

主要方法:

  • 开发了一种灵敏度驱动的尺寸适应性稀疏电网插值策略.
  • 通过适应性利用模型结构 (内在的维度,异型合)
  • 将该方法应用于磁性限制托卡马克中的流传输,其中有八个不确定的参数.

主要成果:

  • 在不确定性量化和灵敏度分析中,至少减少了两个数量级的计算力度.
  • 实现了一个精确的代孕模型,比高保真模型便宜九倍.
  • 证明了自适应方法的效率和可扩展性.

结论:

  • 灵敏度驱动的自适应稀疏电网方法显著提高了对复杂模型的不确定性量化和灵敏度分析的可行性.
  • 这种方法可以节省大量的计算成本,特别是在复杂的领域,如核聚变研究.
  • 开发的代孕模型为未来的调查提供了一个高效的工具.