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相关概念视频

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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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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用数据增强估计不确定性,用于健康数据上的积极学习任务.

Sotirios Vavaroutas, Lorena Qendro, Cecilia Mascolo

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    概括

    这项研究引入了一个使用数据增强来改善医疗保健中的机器学习 (ML) 的新框架. 它增强了信息医疗信号的选择,减少了手动标签的需要,并提高了ML模型的准确性.

    科学领域:

    • 生物医学工程 生物医学工程
    • 机器学习 机器学习
    • 数据科学数据科学数据科学

    背景情况:

    • 医疗保健中的监督机器学习 (ML) 面临着手动,昂贵和耗时的医疗传感器信号数据标签的挑战.
    • 积极学习策略旨在通过查询不确定的样本来减少标签工作,但目前的方法效率低下.

    研究的目的:

    • 开发一个新的框架,用数据增强来估计传感器信号的不确定性.
    • 提高医疗保健ML应用中的积极学习的效率和有效性.

    主要方法:

    • 利用数据增强技术来估计医疗传感器信号的不确定性.
    • 开发了一个新的框架,用于选择有信息的,未标记的样本,用于积极学习.
    • 在医疗信号分类任务中,将拟议的框架与基线方法进行比较.

    主要成果:

    • 与现有方法相比,拟议的框架选择了更多的多样性和信息性样本,达到多达50%的多样性.
    • 该框架通过在查询过程的早期选择具有更高平均点距离的未标记样本来证明优异的样本选择.
    • 基于增量的不确定性估计通过优先考虑信息信号和多样化的特征,导致更好的决策.

    结论:

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  • 新的框架增强了在医疗保健中积极学习的信息样本的选择.
  • 通过基于数据增强的不确定性估计改进的样本选择加速了模型训练,提高了准确性.
  • 这种方法有可能通过减少数据标签负担来扩大ML在现实世界医疗保健场景中的应用.