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相关概念视频

Uncertainty: Overview00:59

Uncertainty: Overview

496
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.
496
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...
621
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

453
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...
453
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

3.1K
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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Dynamic Equilibrium02:20

Dynamic Equilibrium

49.9K
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
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Stability of Equilibrium Configuration01:23

Stability of Equilibrium Configuration

417
Understanding the stability of equilibrium configurations is a fundamental part of mechanical engineering. In any system, there are three distinct types of equilibrium: stable, neutral, and unstable.
A stable equilibrium occurs when a system tends to return to its original position when given a small displacement, and the potential energy is at its minimum. An example of a stable equilibrium is when a cantilever beam is fixed at one end and a weight is attached to the other end. If the weight...
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相关实验视频

Updated: May 24, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

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在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 Shappell, Mark Kramer, Catherine Chu

    bioRxiv : the preprint server for biology
    |March 3, 2025
    PubMed
    概括

    我们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),以计算网络数据中的噪声. 这种新的方法可以提高动态网络 (包括功能性大脑网络) 的估计准确性.

    科学领域:

    • 网络科学 网络科学
    • 计算统计学 计算统计学
    • 神经科学是一个神经科学.

    背景情况:

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

    研究的目的:

    • 为SAOMs提出一个隐藏马尔科夫模型 (HMM) 扩展,以处理杂的网络观测.
    • 在拟议的HMM-SAOM框架中开发一个预期最大化算法用于参数估计.
    • 为了提高在存在观察噪声时的网络动态估计的准确性.

    主要方法:

    • 开发了一个由两个组成部分组成的模型:真实网络演变的潜在马尔科夫过程和观察到的网络的测量模型.
    • 使用预期最大化算法进行参数估计.
    • 利用缺失信息原理和粒子过来管理大状态空间的计算挑战.

    主要成果:

    • 模拟研究表明,与标准SAOM相比,当数据杂时,估计准确度有所提高.
    • 通过HMM-SAOM方法,从EEG数据中获得的功能性大脑网络中发现了更大的效果大小.
    • 拟议的方法优于对杂网络数据的标准SAOM的天真应用.

    更多相关视频

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    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

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    Quantitative Analysis of Cell Edge Dynamics during Cell Spreading
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    Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

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    Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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    结论:

    • 对SAOM的HMM扩展提供了一个更强大的框架来分析具有不完美的观测的动态网络.
    • 这种方法为网络推断的准确性提供了显著的改进,特别是在神经科学等领域.
    • 准确的网络动态建模,即使有噪声,对于可靠的科学发现至关重要.