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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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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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Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
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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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Instrument Calibration

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Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
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相关实验视频

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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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转移学习与不确定性量化:源到目标的随机效应校准 (RECaST)

Jimmy Hickey1, Jonathan P Williams2, Emily C Hector1

  • 1Department of Statistics, North Carolina State University.

Journal of machine learning research : JMLR
|December 15, 2025
PubMed
概括

我们介绍了RECaST,这是一个用于转移学习的新型统计框架,它为新人群重新校准模型. 这种方法提供了至关重要的不确定性量化,与许多现有方法不同.

关键词:
贝叶斯转移学习是贝叶斯的转移学习.电子健康记录是电子健康记录.信息化的贝叶斯先验贝叶斯先验模型校准模型的校准.

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科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 转移学习适应了在一个数据集上训练的模型,以便与另一个数据集一起使用.
  • 当前的转移学习方法往往缺乏不确定性量化.
  • 微调预训练的神经网络是一种常见但有限的方法.

研究的目的:

  • 开发一个统计框架转移学习的不确定性量化.
  • 引入RECaST (统计转移用考奇随机效应重新校准) 框架.
  • 为了证明RECaST在不同模型类型中的有效性和稳定性.

主要方法:

  • 开发了一个统计框架,RECaST,利用Cauchy随机效应进行模型重新校准.
  • 对线性模型进行数学和经验验证的RECaST,确保预测集覆盖.
  • 对非线性模型的数值说明强度,显示对非对称近似的弹性.

主要成果:

  • RECaST为转移学习预测提供了不确定性量化.
  • 该框架不依赖源模型,不需要访问源数据.
  • 通过模拟和现实世界的医院数据分析来证明RECaST的有效性.

结论:

  • 在统计学上,RECaST提供了一种可靠和多样化的学习转移方法.
  • 加入不确定性量化解决了现有方法中的一个关键缺口.
  • RECaST显示出可靠的模型适应在不同种群和数据类型的承诺.