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Uncertainty: Overview00:59

Uncertainty: Overview

525
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.
525
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

3.1K
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...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

653
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...
653
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: 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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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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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...
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相关实验视频

Updated: Jun 7, 2025

An R-Based Landscape Validation of a Competing Risk Model
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独一无二:一个不确定性量化基准测试的框架

Jessica Lanini1, Minh Tam Davide Huynh1, Gaetano Scebba1

  • 1Novartis Biomedical Research, Novartis Campus, 4002 Basel, Switzerland.

Journal of chemical information and modeling
|November 14, 2024
PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 中的不确定性量化 (UQ) 对于科学中可靠的预测至关重要. 唯一的框架标准化了UQ基准测试,以提高模型评估和可靠性在新的应用程序.

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科学领域:

  • 计算化学是一种计算化学.
  • 数据科学是数据科学.
  • 药物发现 药物发现

背景情况:

  • 机器学习 (ML) 模型是药物发现等领域决策的组成部分.
  • 在现实场景中评估ML模型的稳定性和预测能力是具有挑战性的.
  • 不确定性量化 (UQ) 对于评估ML模型可靠性至关重要,但缺乏通用策略.

研究的目的:

  • 引入UNIQUE (不确定性量化对比) 框架,用于在ML中比较UQ策略.
  • 提供一种标准化的方法,用于在各种应用中评估UQ方法.
  • 促进新的UQ方法的开发和评估.

主要方法:

  • 开发唯一的Python库,以统一UQ指标基准测试.
  • 实施标准和非标准的UQ指标,整合数据集和模型信息.
  • 在各种应用场景中评估UQ指标,包括基于信任的过和获取功能.

主要成果:

  • 独一无二的框架允许对多种UQ策略进行全面的基准测试.
  • 它允许计算针对特定应用程序量身定制的新型UQ指标.
  • 该图书馆促进了对UQ绩效的一致而彻底的评估.

结论:

  • 唯一的框架在基于ML的预测中标准化了UQ调查.
  • 它有助于为不同的任务和数据集选择合适的UQ指标.
  • 这种工具将提高ML模型在科学研究中的可靠性和适用性.