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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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Probability Laws01:49

Probability Laws

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Overview
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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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Probability in Statistics01:14

Probability in Statistics

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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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Poisson Probability Distribution01:09

Poisson Probability Distribution

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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
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Binomial Probability Distribution01:15

Binomial Probability Distribution

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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
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相关实验视频

Updated: Jul 25, 2025

Observation and Analysis of Blinking Surface-enhanced Raman Scattering
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Observation and Analysis of Blinking Surface-enhanced Raman Scattering

Published on: January 11, 2018

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任意命令的一般非局部概率

Vasily E Tarasov1,2

  • 1Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow 119991, Russia.

Entropy (Basel, Switzerland)
|June 28, 2023
PubMed
概括

本研究引入了使用分数计算的非局部概率概括. 它定义了概率函数的分数扩展,使得概率论的应用范围更广.

科学领域:

  • 数学 数学 是一个数学.
  • 可能性理论概率理论.
  • 分数微积分的计算.

背景情况:

  • 传统的概率理论通常假定局部相互作用.
  • 一般分数计算 (GFC) 为非局部现象提供了工具.
  • 现有的GFC框架可能会限制非本地概率模型的范围.

研究的目的:

  • 提出概率论的一个非局部概括.
  • 使用分数计算扩展诸如概率密度函数 (PDF) 和累积分布函数 (CDF) 等概念.
  • 在概率上探索更广泛的非本地运营商.

主要方法:

  • 使用卢奇科的一般分数计算 (GFC).
  • 使用GFC的多核扩展用于任意订单 (AO的GFC).
  • 定义和分析概率函数的非局部和一般分数扩展.

主要成果:

  • 成功定义了PDF和CDF的非局部和一般分数 (CF) 扩展.
  • 证明了这些新型概率分布的属性.
  • 引入了一个框架,可以考虑更广泛的运营商内核类别和非本地性.

结论:

关键词:
分数衍生品 分数衍生品.分数积分数的整数.一般的微积分微积分计算.非本地概率的概率.概率理论是概率理论.

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  • 提出的基于GFC的方法提供了一个强大的非局部概括概率.
  • 这一框架扩大了概率论的适用性,使其适用于非局部性更复杂的系统.
  • 多核GFC进一步提高了这些非本地概率模型的灵活性和范围.