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相关概念视频

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

445
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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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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The Uncertainty Principle04:08

The Uncertainty Principle

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Uncertainty in Measurement: Accuracy and Precision03:37

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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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Updated: May 15, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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最小的不确定性作为贝叶斯网络模型选择原则.

Grigoriy Gogoshin1, Andrei S Rodin2

  • 1Department of Computational and Quantitative Medicine, Beckman Research Institute, and Diabetes and Metabolism Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA, 91010, USA. ggogoshin@coh.org.

BMC bioinformatics
|April 8, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一个新的最小不确定性 (MU) 模型选择原则,用于贝叶斯网络 (BN) 重建. MU克服了数据不可衡量的问题,提高了BN的解释性,并使直接比较成为可能.

关键词:
在AICIC AICIC中,您可以使用AICIC.在这里,APOEOE是APOE.这就是为什么BD是BD.在BIC BIC中,我们可以看到.贝叶斯网络是一个贝叶斯网络.有条件的独立性 有条件的独立性在MDL中,MDL是MDL.模型选择标准 模型选择标准互助信息互助信息互助信息互助信息概率网络是一种概率网络.采样错误 采样错误 采样错误统计不确定性 统计不确定性这是一个tRNARNA.

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Last Updated: May 15, 2025

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科学领域:

  • 计算系统生物学 计算机系统生物学
  • 生物信息学是一种生物信息学.
  • 数据科学数据科学数据科学

背景情况:

  • 贝叶斯网络 (BN) 建模在系统生物学中至关重要.
  • 数据集的不相称性导致模型选择标准中的不规则,例如最小描述长度 (MDL).
  • 这阻碍了BN模型特征的解释和比较,例如依赖强度.

研究的目的:

  • 推导和评估贝叶斯网络的新型模型选择原则.
  • 解决BN重建中的上下文依赖性和数值不规则问题.
  • 提高BN模型的可解释性和可比性.

主要方法:

  • 框架模型评估作为一个错误规范问题.
  • 估计采样错误对条件独立性的影响,使用相互信息.
  • 发展最小不确定性 (MU) 原则,以惩罚不确定性.

主要成果:

  • 最小不确定性 (MU) 标准显示了比现有方法的性能优势.
  • 数字验证证实了MU原则的有效性.
  • 现实世界的数据示例说明了新的评估框架的好处.

结论:

  • MU原则解决了在MDL中发现的绩效不规则.
  • 它为BN重建提供了更好的收率.
  • MU增强了BN的解释性和普遍性,促进了BN之间的直接比较.