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相关概念视频

Per-Unit Sequence Models01:26

Per-Unit Sequence Models

481
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
481
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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Prediction Intervals01:03

Prediction Intervals

3.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Random Variables01:09

Random Variables

18.4K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
18.4K
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...
1.5K
Random Error01:04

Random Error

10.0K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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相关实验视频

Updated: Mar 13, 2026

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

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对任意序列进行校准的概率预测.

Charles Marx1, Volodymyr Kuleshov2, Stefano Ermon1

  • 1Department of Computer Science, Stanford University.

Transactions on machine learning research
|March 12, 2026
PubMed
概括
此摘要是机器生成的。

本研究引入了一种使用游戏理论的新预测框架,以确保可靠的不确定性估计,即使有不可预测的数据变化. 该方法保证了校准的预测,并改善了在能源系统等现实应用中的决策.

相关实验视频

Last Updated: Mar 13, 2026

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

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科学领域:

  • 机器学习 机器学习
  • 游戏理论 游戏理论
  • 数据科学数据科学数据科学

背景情况:

  • 现实世界的数据流面临着不可预测的变化 (分布转移,反循环,对抗性行为者).
  • 这些变化挑战了现有的预测方法的有效性和可靠性.
  • 确保准确的不确定性估计对于可靠的决策至关重要.

研究的目的:

  • 开发一个预测框架,提供有效的不确定性估计,无论数据的演变.
  • 为了保证在紧的空间中对结果的校准不确定性.
  • 扩大用于重新校准现有预测器的框架,而不会造成性能损失.

主要方法:

  • 从游戏理论中利用布莱克韦尔的可接近性.
  • 开发一种基于梯度的通用算法.
  • 为框架的特殊情况优化算法.
  • 为现有预测者实施重新校准技术.

主要成果:

  • 该框架为任何紧的结果空间保证了校准的不确定性.
  • 再校准的预报器在不牺牲预测性能的情况下实现校准.
  • 经验结果表明,能源系统的校准和决策得到了改进.

结论:

  • 拟议的框架确保在动态数据环境中有效估计不确定性.
  • 这种方法提高了预测和下游决策的可靠性.
  • 这些方法适用于各种预测任务,包括分类和局限回归.