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相关概念视频

Propagation of Uncertainty from Random Error00:59

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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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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 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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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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Actor-Observer Effect

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The actor-observer effect, a cognitive bias closely linked to the fundamental attribution error, refers to the tendency for individuals to attribute their behavior to external, situational factors while explaining others’ behavior in terms of internal, dispositional traits. This asymmetry in attribution significantly influences social perception and judgment.Cognitive Mechanisms Behind the EffectTwo primary psychological mechanisms contribute to the actor-observer effect: differences in...
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Actuarial Approach01:20

Actuarial Approach

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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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相关实验视频

Updated: Mar 7, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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在用于动态网络分析的随机行为者导向模型中计算边缘不确定性.

Heather M Shappell1, Mark A Kramer2, Catherine J Chu3

  • 1Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston Salem, NC, USA.

Network science (Cambridge University Press)
|March 6, 2026
PubMed
概括

本研究引入了一个隐藏的马尔科夫模型 (HMM) 扩展到随机行为者导向模型 (SAOMs),以准确分析杂的网络数据. 这种新方法可以提高动态网络 (包括功能性脑网络) 的估计准确度.

关键词:
大脑网络 大脑网络预期最大化算法是指期望最大化算法.隐藏的马尔科夫模型纵向网络分析 纵向网络分析颗粒过器 颗粒过器社交网络 社交网络

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科学领域:

  • 网络分析 网络分析
  • 统计建模 统计建模
  • 计算神经科学是一种计算神经科学.

背景情况:

  • 随机行为者导向模型 (SAOM) 是分析社交网络动态的标准.
  • 在SAOM中假设无错误的网络数据通常是不现实的.
  • 现实世界的网络经常包含假阳性和假阴性边缘.

研究的目的:

  • 开发SAOM的扩展,以考虑观察到的网络数据中的噪声.
  • 为了提高在存在测量错误的情况下对网络变化估计的准确性.
  • 将新方法应用于功能性大脑网络分析.

主要方法:

  • 一个隐藏的马尔科夫模型 (HMM) 框架,集成真实网络的隐藏马尔科夫过程和观察到的网络的测量模型.
  • 一个预期最大化算法用于参数估计.
  • 颗粒过和缺失信息原理来处理大状态空间.

主要成果:

  • 拟议的HMM-SAOM扩展显示,与噪音网络数据的标准SAOM相比,估计准确度有所提高.
  • 模拟研究证实,在存在测量错误的情况下,性能提高.
  • 对脑电图 (EEG) 数据的应用揭示了功能性大脑网络中更大的效果大小.

结论:

  • 该HMM-SAOM扩展提供了一个更强大的方法来分析动态网络与杂的观测.
  • 这种方法比标准的SAOMs具有显著的优势,特别是在神经科学等领域.
  • 准确的网络动态建模与测量误差对于可靠的科学推断至关重要.