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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

11.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.6K
Probability Histograms01:17

Probability Histograms

11.1K
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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Bar Graph01:07

Bar Graph

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
16.0K
Modified Boxplots00:57

Modified Boxplots

9.1K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
9.1K
Probability Distributions01:32

Probability Distributions

6.8K
 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...
6.8K
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

660
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
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相关实验视频

Updated: Jun 4, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

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图形迪里克莱特过程用于聚类不可交换的分组数据.

Arhit Chakrabarti1, Yang Ni2, Ellen Ruth A Morris3

  • 1Department of Statistics, Texas A&M University, College Station, TX 77843-3143, USA.

Journal of machine learning research : JMLR
|December 18, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于集群不可交换的分组数据的图形狄里克莱特过程. 这种贝叶斯式方法使得跨依赖组的集群共享成为可能,改善了复杂数据集的分析.

关键词:
贝叶斯的非参数.聚类集群是指聚类的聚类.定向非循环图是指向的非循环图.家庭拥有的餐厅餐厅的过程.不能交换的组别是不可交换的组.

更多相关视频

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

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相关实验视频

Last Updated: Jun 4, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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科学领域:

  • 统计 统计 统计 统计
  • 计算生物学 计算生物学
  • 机器学习 机器学习

背景情况:

  • 将分组数据与不可交换组进行聚类,会带来分析挑战.
  • 现有的方法往往难以建模群体之间的复杂依赖关系.

研究的目的:

  • 提出一种新的贝叶斯非参数方法,用于对依赖关系的分组数据进行集群.
  • 为了使非可交换组之间能够共享集群,使用指向非循环图结构.

主要方法:

  • 介绍了图形的迪里克莱特过程,贝叶斯的非参数模型.
  • 利用定向非循环图来描述特定群体随机测量之间的依赖关系.
  • 开发了一种有效的后置推理算法,用于模型估计.

主要成果:

  • 图形的迪里克莱特过程联合模型依赖于特定组的随机测量.
  • 该模型尊重导向非循环图的马尔科夫属性.
  • 通过模拟和分析单细胞数据来证明模型的实用性.

结论:

  • 图形的迪里克莱特过程提供了一个灵活的框架,用于集群复杂的分组数据.
  • 该方法有效地处理不可交换的组及其依赖关系.
  • 适用于各种领域,包括生物信息学和单细胞数据分析.