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Uncertainty: Overview00:59

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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

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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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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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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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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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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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    科学领域:

    • 计算机视觉 计算机视觉
    • 医学研究 医学研究
    • 机器学习 机器学习

    背景情况:

    • 视线估计对于计算机视觉和医疗应用至关重要.
    • 现有的方法往往忽略了输入和注释的不确定性,导致确定性预测.
    • 这种限制阻碍了当前视线估计模型的实际应用.

    研究的目的:

    • 开发一个概率框架,用于注视估计,考虑到输入和注释的不确定性.
    • 为预测中的信心测量引入实例智能不确定性估计.
    • 在现实场景中提高视线估计的准确性和可靠性.

    主要方法:

    • 概率嵌入用于建模输入不确定性,将图像表示为高斯分布.
    • 一种新的标签分布学习方法,概率注释,使用高斯分布模型注释不确定性.
    • 开发了一个嵌入式分布平滑 (EDS) 模块和硬例采矿,以提高分布的一致性.

    主要成果:

    • 拟议的概率框架显著提高了目光估计的准确性.
    • 该方法在基准数据集 (GazeCapture,MPIIFaceGaze) 和移动数据上表现优于基准和最先进的方法.
    • 对实例的不确定性估计为预测提供了关键的信心指标.

    结论:

    • 概率框架有效地建模并解决了目光估计中的不确定性.
    • 这种方法提供了更可靠和可解释的目光预测结果.
    • 这些发现对推进计算机视觉和眼睛跟踪医学研究具有重大意义.