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相关概念视频

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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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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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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Estimation of the Physical Quantities01:05

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Interpretation of Confidence Intervals01:19

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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不确定性 数据充足性的网络推理量化

Bharat Singhal1, Jorge Luis Ocampo-Espindola2, K L Nikhil3

  • 1Department of Electrical and Systems Engineering, Washington University in St Louis, St. Louis, Missouri 63130, USA.

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概括
此摘要是机器生成的。

确定数据充足度对于准确的网络推断至关重要. 本研究引入了一种使用置信区间量化数据可变性的统计方法,确保可靠的网络拓重建.

关键词:
自信区间网络推断网络拓非线性振荡器

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科学领域:

  • 复杂系统科学
  • 网络科学
  • 统计推断

背景情况:

  • 网络推断从数据中重建系统连接,对于理解物理,生物和化学系统至关重要.
  • 目前的数据驱动方法往往忽视了准确网络拓的数据充足性的关键问题.
  • 准确的网络重建需要足够的数据可变性来可靠地推断底层结构.

研究的目的:

  • 开发一个统计方法来评估网络推断中的数据充分性.
  • 根据数据变化量化推断网络连接的不确定性.
  • 确保推断的网络拓准确地反映了真正的基础网络结构.

主要方法:

  • 使用参数置信区间来定义真实连接强度的边界.
  • 开发一种技术来评估网络推断准确性的数据可变性.
  • 借助推断连接的不确定性量化.

主要成果:

  • 拟议的统计方法有效地确定了网络推断的数据充分性.
  • 在Kuramoto和Stuart-Landau振荡器网络上的验证证明了方法的准确性.
  • 对实验电化学振荡器网络数据的成功应用证实了预测能力.

结论:

  • 开发的数据充足技术对于可靠的网络推断至关重要.
  • 这种方法提高了网络拓重建的可靠性.
  • 它提供了一种定量衡量,以确保足够的数据用于准确的系统分析.