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相关概念视频

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: 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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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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Estimating Population Standard Deviation01:26

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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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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在基于微生物组的预测中使用高斯过程与微生物社区不相似性的量化不确定性.

Asahi Adachi1, Fan Zhang1, Shigehiko Kanaya1,2

  • 1Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma 630-0192, Japan.

Bioinformatics advances
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概括

我们开发了一个新的高斯过程 (GP) 模型来量化人类微生物组预测中的不确定性. 这种概率方法提高了基于微生物组的健康和疾病预测在临床环境中的可靠性.

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科学领域:

  • 微生物组研究的研究.
  • 机器学习应用程序 机器学习应用程序
  • 计算生物学是一种计算生物学.

背景情况:

  • 人类微生物组与宿主健康和疾病密切相关.
  • 机器学习模型越来越多地用于从微生物组数据中预测健康状况.
  • 量化预测不确定性对于临床应用至关重要,但仍未得到充分发展.

研究的目的:

  • 开发人类微生物组的概率预测模型.
  • 将微生物社区的差异纳入预测模型.
  • 评估模型在量化预测不确定性的表现.

主要方法:

  • 开发了一种高斯过程 (GP) 模型,用于微生物社区差异的内核功能.
  • 将模型应用于回归任务,包括时间表年龄,体重指数和疾病严重程度.
  • 利用公开可用的人类肠道微生物组数据集进行评估.

主要成果:

  • 与现有方法相比,开发的GP模型显示出更高的概率预测准确性.
  • 模型中的信心水平与经验覆盖率有很强的相关性.
  • 不确定性较低的预测与预测错误的减少相对应.

结论:

  • 纳入社区不相似性的高斯过程回归模型有效地处理了家族遗传,高维和稀疏的微生物组数据.
  • 这项研究为基于微生物组的预测提供了更可靠的框架.
  • 这一进步有可能通过微生物组数据改善健康监测和疾病诊断.