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相关概念视频

Student t Distribution01:31

Student t Distribution

5.9K
The population standard deviation is rarely known in many day-to-day examples of statistics. When the sample sizes are large, it is easy to estimate the population standard deviation using a confidence interval, which provides results close enough to the original value. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
The Student t distribution was developed by William S. Goset (1876–1937) of the...
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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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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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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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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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相关实验视频

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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
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研究学生的T分布点云注册算法基于本地特征的研究.

Houpeng Sun1, Yingchun Li2, Huichao Guo2

  • 1Graduate School, Space Engineering University, Beijing 101416, China.

Sensors (Basel, Switzerland)
|August 10, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的算法来处理来自LiDAR的3D点云数据,通过使用学生的t分布来提高注册的准确性和稳定性. 该方法有效地处理LiDAR应用中常见的噪声和数据丢失.

关键词:
学生的t分布混合模型.适应性惩罚是适应性的惩罚.复合重量系数的复合重量系数地方特征 地方特征 地方特征点云注册点云注册是什么意思

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相关实验视频

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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

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Sample Drift Correction Following 4D Confocal Time-lapse Imaging
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科学领域:

  • 地理空间技术是什么?
  • 计算机视觉 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 激光雷达 (光检测和测距) 对于自主系统和绘图至关重要.
  • 立达3D点云注册面临着噪音,数据丢失和混乱等挑战.

研究的目的:

  • 为LiDAR数据开发一个新的点云注册算法.
  • 为了解决3D点云注册中的噪音,数据丢失和混乱问题.

主要方法:

  • 使用学生的t分布混合模型 (SMM) 进行概率分布.
  • 集成的本地点云功能用于目标功能设计.
  • 雇员适应点对点和点对平面距离处罚.
  • 添加了一个基于LiDAR成像特征的复合重量系数.

主要成果:

  • 拟议的算法证明了高可行性和可靠性.
  • 在准确性和稳定性方面超过了五个比较算法.
  • 成功处理了点云中的缺失数据和数据失序.

结论:

  • 小说学生的t分布算法增强了LiDAR 3D点云注册.
  • 该方法为自动驾驶和城市规划等应用提供了更高的准确性和稳定性.