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相关概念视频

Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

454
Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
454
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.0K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

698
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...
698
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

528
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...
528
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106

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相关实验视频

Updated: Jul 8, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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双重精度-质量驱动的神经网络用于预测间隔生成.

Giorgio Morales, John W Sheppard

    IEEE transactions on neural networks and learning systems
    |December 19, 2023
    PubMed
    概括

    本研究引入了深度学习回归模型的新方法,以自动生成高质量的预测间隔 (PI). 该方法确保了狭窄和准确的PI,提高了现实应用中的模型可靠性.

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 准确的不确定性量化对于可靠的深度学习 (DL) 模型部署至关重要.
    • 预测间隔 (PI) 对于回归任务至关重要,为预测提供了一个范围.
    • 高质量的PI必须是狭窄的,并准确地捕捉概率密度.

    研究的目的:

    • 为基于回归的神经网络 (NN) 自动学习预测间隔 (PI) 开发一种方法.
    • 通过确保它们是狭窄的,并有效地覆盖真实价值来提高PI的质量.
    • 在现实世界的回归应用中提高DL模型的可靠性.

    主要方法:

    • 训练两个伴侣神经网络:一个用于目标估计,另一个用于PI边界.
    • 为PI网络设计一种新的损失函数,其目标是尽量减少PI宽度并确保PI覆盖范围.
    • 实施自适应系数以平衡损失函数的优化目标.

    主要成果:

    • 提出的方法成功地产生了预测间隔,并覆盖了名义概率.
    • 与最先进的方法相比,该方法产生了明显更窄的PI.
    • 目标估计的准确性保持不损害,导致更高质量的PI.

    更多相关视频

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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    Deep Neural Networks for Image-Based Dietary Assessment
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    Deep Neural Networks for Image-Based Dietary Assessment

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    相关实验视频

    Last Updated: Jul 8, 2025

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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    Deep Neural Networks for Image-Based Dietary Assessment
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    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

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    结论:

    • 这种新的方法有效地学习深度学习回归模型的高质量预测间隔.
    • 该方法通过提供准确和狭窄的不确定性估计来提高模型可靠性.
    • 这项工作通过改进不确定性量化,为更可靠的AI应用做出了贡献.