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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 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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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

99.7K
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. 
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Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

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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: Jan 15, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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使用贝叶斯式机器学习方法量化人类活动识别的不确定性:一个预测研究.

Hiroshi Mamiya1, Daniel Fuller2

  • 1Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada.

Physical activity and nutrition
|October 15, 2025
PubMed
概括

贝叶斯增量回归树 (BART) 是一种新的机器学习方法,可以从可穿戴设备数据准确预测身体活动状态. 这一进步使得体力活动预测能够更好地融入到建筑环境研究中.

关键词:
贝叶斯增量回归树是贝叶斯的增量回归树.机器学习是机器学习.人口 身体活动 人口 身体活动穿戴式设备是一种可穿戴的设备.

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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相关实验视频

Last Updated: Jan 15, 2026

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科学领域:

  • 机器学习在公共卫生中的应用.
  • 穿戴式传感器数据分析数据分析
  • 环境健康研究环境健康研究

背景情况:

  • 机器学习使用可穿戴设备的加速度计数据准确预测身体活动.
  • 研究建筑环境对人口体力活动的影响至关重要.
  • 传统方法缺乏预测不确定性量化,与贝叶斯增量回归树 (BART) 不同.

研究的目的:

  • 评估贝叶斯增量回归树 (BART) 预测身体活动状态的性能.
  • 评估BART在量化预测不确定性的能力.
  • 探索BART在建筑环境研究中的潜力.

主要方法:

  • 应用多项BART和随机森林对37名参与者 (25,424个时间点) 的加速度计数据.
  • 为了绩效评估,使用了"离开一个人"的交叉验证.
  • 评估预测准确度,F1分数和混矩阵.

主要成果:

  • 无论是BART还是随机森林都表现出了可比的预测性能.
  • 通过后预测分布,BART成功量化了预测不确定性.
  • 这些方法在预测身体活动状态方面表现出很高的准确性.

结论:

  • BART是一种有前途的机器学习方法,用于预测身体活动状态.
  • 巴特可以增强预测体育活动在建筑环境研究中的整合.
  • 未来的研究应该探索环境和BART预测的体育活动之间的关联.