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相关概念视频

Propagation of Uncertainty from Systematic Error01:10

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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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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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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: 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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Variability: Analysis01:11

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
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相关实验视频

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An R-Based Landscape Validation of a Competing Risk Model
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通过树结构学习改善变异网络的不确定性量化.

Wenxuan Ma, Xing Yan, Kun Zhang

    IEEE transactions on neural networks and learning systems
    |December 21, 2023
    PubMed
    概括

    我们介绍了不确定性分割神经回归树 (USNRT),这是一个改进不确定性定量化的新型模型. 实际上,USNRT分区具有空间,可以更好地预测差异,并提高模型可靠性.

    科学领域:

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

    背景情况:

    • 不确定性量化对于可靠的机器学习模型至关重要.
    • 现有的方差网络方法在捕捉复杂的不确定性模式方面存在局限性.

    研究的目的:

    • 开发一种新的树结构神经网络模型,用于增强不确定性量化.
    • 解决特征空间内不确定性的异质性挑战.

    主要方法:

    • 提出了不确定性分割神经回归树 (USNRT) 模型.
    • USNRT分区将空间划分为基于不确定性异质性的区域.
    • 特定区域的神经网络预测了平均值和差异;基于残余分析的新型分割标准被采用.

    主要成果:

    • 与UCI数据集上的现有方法相比,USNRT在不确定性量化方面表现优越.
    • 该模型有效地识别和学习不确定性异质性.
    • 在计算上USNRT是高效的,不需要修剪.

    结论:

    • USNRT提供了一种强大而有效的方法来量化不确定性.

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  • 模型学习不确定性异质性的能力提高了预测可靠性.
  • 组合版本可以捕捉到异常和认识的不确定性.