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相关概念视频

Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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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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P-value01:10

P-value

7.0K
P-value is one of the most crucial concepts in statistics.
P-value stands for the probability value.  P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to  not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...
7.0K
Probability Laws01:49

Probability Laws

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Overview
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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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相关实验视频

Updated: Jul 25, 2025

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
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Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

Published on: November 14, 2017

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一种新的证据组合方法,基于改进的皮尼斯学概率.

Xin Shi1, Fei Liang1, Pengjie Qin1

  • 1School of Automation, Chongqing University, Chongqing 400044, China.

Entropy (Basel, Switzerland)
|June 28, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的方法,用于结合相互矛盾的证据,在单一目标识别中使用改进的猪视概率函数. 这种新的方法提高了证据融合的准确性,并减少了证据融合的复杂性.

关键词:
DS证据理论的证据理论信息融合 信息融合 信息融合猪的概率函数是一个概率函数.

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相关实验视频

Last Updated: Jul 25, 2025

Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
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Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

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科学领域:

  • 人工智能的人工智能
  • 信息融合 信息融合
  • 模式识别 模式识别

背景情况:

  • 证据理论对于不确定的信息融合至关重要.
  • 在单一目标识别中,相互矛盾的证据融合是一个重大挑战.
  • 现有的方法与计算复杂性和信息丢失作斗争.

研究的目的:

  • 为单个目标识别提出一种新的证据组合方法.
  • 为了有效地解决融合相互矛盾的证据的挑战.
  • 为了提高准确性和减少证据融合中的计算负担.

主要方法:

  • 为证据重新分配开发了一种改进的猪态概率函数.
  • 利用曼哈顿距离和证据角度以获得确定性和相互支持的提取.
  • 使用来计算不确定性,使用加权平均来纠正证据.
  • 应用Dempster的结合规则来最终结合证据.

主要成果:

  • 与现有技术相比,拟议的方法显示了更好的趋同.
  • 与基准方法相比,平均准确度提高了0.51%和2.43%.
  • 在单个和多个子集的命题中有效处理高度相互矛盾的证据.

结论:

  • 新的证据结合方法为冲突的证据融合提供了强有力的解决方案.
  • 该方法提高了单个目标识别的准确性和效率.
  • 这项工作有助于在不确定性下推进信息融合领域.