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相关概念视频

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Probability Distributions01:32

Probability Distributions

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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Uniform Distribution01:19

Uniform Distribution

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The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.
Two essential properties of this distribution are
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Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

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The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Sampling Distribution01:12

Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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相关实验视频

Updated: Jul 9, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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基于弱监督学习的可见性估计,在离散标签分布下进行.

Qing Yan1, Tao Sun1, Jingjing Zhang1

  • 1The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的神经网络,通过分析不均的雾分布来估计雾图像中的可见性. 该模型有效地学习区域可见性差异,提高自动驾驶系统的准确性.

关键词:
深度学习是一种深度学习.标签分发学习学习可见度估计可见度估计缺乏监督的学习学习.

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 图像处理 图像处理

背景情况:

  • 在雾中估计可见度对于安全导航至关重要,特别是对于自动驾驶系统.
  • 现有的方法经常与复杂和不均的雾分布作斗争.
  • 准确的可见度估计需要了解模糊图像中的区域差异.

研究的目的:

  • 提出一个端到端的神经网络模型,用于准确估计雾图像中的可见性.
  • 为了提高性能,利用雾分布不均的特点.
  • 为实际应用开发一种具有较低注释要求的可靠方法.

主要方法:

  • 将单一标签转化为离散标签分布,用于区域分析.
  • 在分类网络中引入离散标签分发学习.
  • 采用双线注意力聚合模块来识别最远的可见雾区域.
  • 利用基于注意力的分支和级的特征融合与基础分支.

主要成果:

  • 拟议的模型有效地通过利用不均的雾分布来估计可见性.
  • 在真实高速公路和合成道路数据集上都表现出有效性.
  • 实现了低注释要求,表明了实际可行性.
  • 展示了良好的稳定性和可见度估计方法的广泛应用空间.

结论:

  • 开发的神经网络模型为雾中可见度估计提供了有效的解决方案.
  • 离散标签分布的学习和注意力机制增强了模型解释复杂雾模式的能力.
  • 该方法的稳定性和较低的注释需求使其适合在各种场景中实际部署.