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相关概念视频

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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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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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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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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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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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
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An R-Based Landscape Validation of a Competing Risk Model
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如何在回归的机器学习中评估不确定性估计?

Laurens Sluijterman1, Eric Cator2, Tom Heskes3

  • 1Department of Mathematics, Radboud University, P.O. Box 9010-59, 6500 GL, Nijmegen, Netherlands.

Neural networks : the official journal of the International Neural Network Society
|March 5, 2024
PubMed
概括

目前用于评估神经网络不确定性估计的评估方法有缺陷. 我们提出一种新的基于模拟的方法,以更好地评估和开发不确定性量化技术.

关键词:
在 Bootstrap 中使用 Bootstrap.放弃 放弃 放弃 放弃神经网络的神经网络的神经网络回归是一种回归.不确定性 不确定性

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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:

  • 机器学习 机器学习
  • 人工智能的人工智能
  • 统计建模 统计建模

背景情况:

  • 神经网络越来越多地使用,需要可靠的不确定性估计.
  • 评估不确定性估计的现有方法 (密度的日志概率,预测间隔的覆盖范围) 有局限性.
  • 这些方法努力解开预测不确定性的组成部分,并比较不同的估计方法.

研究的目的:

  • 识别和分析当前用于评估来自神经网络的不确定性估计方法的基本缺陷.
  • 为了证明日志概率和直接预测间隔测试的局限性.
  • 提出一种新的基于模拟的方法,用于更强大,更可比的不确定性量化评估.

主要方法:

  • 现有评估指标的理论分析 (日志概率,预测间隔覆盖范围).
  • 模拟来证明当前方法的缺陷,包括边际与点向点覆盖范围的问题.
  • 开发和提出基于模拟的测试框架,用于不确定性量化.

主要成果:

  • 逻辑概率和直接预测间隔测试都表现出重大缺陷.
  • 目前的方法无法可靠地评估预测不确定性的单个组成部分.
  • 用单个数据集进行测试可以掩盖不良行为,如过度/不足的自信.
  • 更好的日志概率并不能保证更好的预测间隔.

结论:

  • 评估神经网络不确定性估计的现有方法是不充分的.
  • 提出一种新的基于模拟的方法来克服这些局限性.
  • 这种新方法有助于更好地比较和开发不确定性量化方法.