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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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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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Reason and Intuition01:37

Reason and Intuition

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Interval Level of Measurement00:55

Interval Level of Measurement

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For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
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Robust Support Vector Data Description with Truncated Loss Function for Outliers Depression.

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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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一个具有云模型,Z数字和间隔值的语言中性学集合的决策模型.

Huakun Chen1,2, Jingping Shi1,2, Yongxi Lyu1,2

  • 1School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.

Entropy (Basel, Switzerland)
|November 27, 2024
PubMed
概括

这项研究引入了一个新的Z-间隔值的语言中性学集合-形-形云 (Z-IVLNS-TTC) 模型,以更好地处理不确定性. 这种新的方法改善了信息量化和复杂情景中的决策.

关键词:
Z-区间值的语言中性学集合-形形云 (Z-IVLNS-TTC)这就是Z数字.集团决策 集团决策区间值的语言中性学集合 (IVLNSs)梯形云模型的云模型

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科学领域:

  • 决策科学 决策科学
  • 信息科学 信息科学 信息科学
  • 人工智能的人工智能

背景情况:

  • 间隔值的语言中性学集合 (IVLNSs),Z数和梯形云是模拟不确定性的关键.
  • 现有的方法在准确量化和处理复杂的不确定的信息方面面临挑战.

研究的目的:

  • 开发一种新的Z-间隔值的语言中性学集合-形-形云 (Z-IVLNS-TTC) 模型.
  • 整合IVLNS和Z数以增强不确定性的表达.
  • 尽量减少信息丢失和量化中的扭曲.

主要方法:

  • 引入了一种IVLNS和Z数字的新组合.
  • 建议采用Z-IVLNS-TTC模型来改进信息表示.
  • 目标权重是使用多目标规划 (MOP) 计算的.
  • 为Z-IVLNS-TTCs开发了一个基于p-norm的距离测量,灵感来自TOPSIS.

主要成果:

  • 拟议的Z-IVLNS-TTC模型有效地减少了信息丢失和扭曲.
  • 介绍了一种使用MOP的新客观权重计算方法.
  • 一种新的距离测量方法可以提高不确定的信息的比较.
  • 该方法在集团决策中证明了其实际适用性.

结论:

  • Z-IVLNS-TTC模型为处理复杂的不确定性提供了一个强大的框架.
  • 开发的方法为在不确定性下做出决策提供了有效的工具.
  • 灵敏度分析和比较证实了该方法的有效性和可行性.