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相关概念视频

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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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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Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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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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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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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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相关实验视频

Updated: Jun 8, 2025

A Tactile Automated Passive-Finger Stimulator TAPS
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这是一个完全贝叶斯的VIB-DeepSSMSSM.

Jadie Adams1,2, Shireen Y Elhabian1,2

  • 1Scientific Computing and Imaging Institute, University of Utah, UT, USA.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|November 6, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一个完全贝叶斯的深度学习框架,用于从3D图像进行统计形状建模 (SSM). 它改善了解剖形状分析的不确定性量化,增强了临床诊断潜力.

关键词:
贝叶斯的深度学习是贝叶斯的深度学习.认识不确定性量化定量化统计形状建模 统计形状建模变化信息瓶 变化信息瓶

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相关实验视频

Last Updated: Jun 8, 2025

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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
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科学领域:

  • 医学成像分析分析 医学成像分析
  • 计算解剖学的计算解剖学
  • 机器学习在医疗保健中的应用

背景情况:

  • 统计形状建模 (SSM) 对于分析解剖变异和帮助临床诊断至关重要.
  • 深度学习模型可以从3D图像中预测SSM,但往往缺乏强大的不确定性量化.
  • 现有变异信息瓶深度SSM (VIB-DeepSSM) 提供了理论上的不确定性,但不是认识体系的不确定性.

研究的目的:

  • 为DeepSSM开发一个完全贝叶斯的变量信息瓶 (VIB) 框架.
  • 实施和评估可扩展的贝叶斯方法,包括具体的脱落和批量组合,以增强不确定性推断.
  • 改进医疗图像的概率形状预测的准确性和不确定性校准.

主要方法:

  • 对DeepSSM的完全贝叶斯VIB公式的导出.
  • 实施混凝土脱落和批组合方法,用于可扩展的认识系统不确定性估计.
  • 引入一种新的混凝土脱落和批量组合的组合,用于多式联运边缘化和改进的不确定性校准.

主要成果:

  • 完全贝叶斯VIB网络准确地从3D图像中预测统计形状模型.
  • 提出的方法有效量化了 Aleatoric 和 Epistemic 的不确定性.
  • 对合成和真实 (左心房) 数据的实验表明,在不影响预测准确性的情况下,不确定性推理得到了改善.

结论:

  • 完全贝叶斯VIB DeepSSM提供了一个原则性的方法,用于从图像中准确和可靠的概率形状建模.
  • 可扩展的贝叶斯实现增强了不确定性量化,这对于SSM的临床应用至关重要.
  • 脱落和组合方法的新组合提供了卓越的不确定性校准.