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相关概念视频

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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Data: Types and Distribution01:19

Data: Types and Distribution

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In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
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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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Construction of Frequency Distribution01:15

Construction of Frequency Distribution

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A frequency distribution table can be constructed using the steps given below.
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is...
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Chi-square Distribution01:10

Chi-square Distribution

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How does one determine if bingo numbers are evenly distributed or if some numbers occurred with a greater frequency? Or if the types of movies people preferred were different across different age groups or if a coffee machine dispensed approximately the same amount of coffee each time. These questions can be addressed by conducting a hypothesis test. One distribution that can be used to find answers to such questions is known as the chi-square distribution. The chi-square distribution has...
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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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综述:统计分布的构建

Kai-Tai Fang1,2, Yu-Xuan Lin1,3, Yu-Hui Deng1,3

  • 1Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science (IRADS), Beijing Normal-Hong Kong Baptist University, 2000 Jintong Road, Tangjiawan, Zhuhai 519087, China.

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概括

本综述探讨了构建概率分布的方法,涵盖传统和先进的技术. 它强调了合理论如何为灵活的统计建模提供复杂的元分布.

关键词:
建设统计分布的建设.家庭分布 家庭分布进入的过程中,这是随机变量的函数.平均平方误差 平均平方误差几乎是蒙特卡洛的样子.代表性点是代表性的点.统计分布的统计分布.

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科学领域:

  • 统计 统计 统计 统计
  • 可能性理论概率理论.

背景情况:

  • 统计模型严重依赖于概率分布.
  • 分布可以是离散的或连续的,和单变量或多变量.

研究的目的:

  • 审查构建概率分布的方法.
  • 涵盖传统和新开发的方法.
  • 讨论配理论在创造元分布中的作用.

主要方法:

  • 检查经典的单变分布 (正常,指数,马,β).
  • 复习经典的多变量分布 (多变量正常,圆,迪里克莱特).
  • 讨论使用合理论构建的元分布.

主要成果:

  • 传统分布为统计分析提供了基础工具.
  • 理论为构建复杂,灵活的分布提供了先进的方法.
  • 超分发解决了对增强模拟能力的需求.

结论:

  • 对分布结构的全面理解对于统计建模至关重要.
  • 方法的进步,特别是偶理论,扩大了建模灵活性.
  • 该审查提供了对既定和新兴配电施工技术的见解.