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相关概念视频

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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Bias01:22

Bias

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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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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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
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在优化模型中制定公平性的指南.

Violet Xinying Chen1, J N Hooker2

  • 1Stevens Institute of Technology, Hoboken, USA.

Annals of operations research
|June 26, 2023
PubMed
概括
此摘要是机器生成的。

本研究调查了将公平性纳入优化模型的数学方法,超越了简单的成本效益分析. 它提出了将道德标准纳入决策过程的实际方法.

关键词:
分配正义是一种分配正义.公平的 公平的 公平的

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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科学领域:

  • 运营研究 运营研究
  • 决策科学 决策科学 决策科学
  • 计算机科学 计算机科学

背景情况:

  • 优化模型传统上专注于最大化收益或最小化成本.
  • 将公平性和道德考虑纳入数学模型中存在重大挑战.
  • 现有的文献提供了各种方法,但缺乏统一的框架.

研究的目的:

  • 对优化中的公平性进行批判性调查和分析各种数学公式.
  • 探索将效率和公平问题整合在一起的方法.
  • 确定在优化模型中实施这些标准的实际方法.

主要方法:

  • 关于优化和社会选择理论中的公平标准的综合文献综述.
  • 对不平等措施,罗尔斯标准,讨价还价解决方案 (如纳什,卡莱-斯莫罗丁斯基) 和群体平价指标的分析.
  • 检查公用事业门和公平门计划.
  • 评估线性,非线性和混合整数编程中的表述.

主要成果:

  • 详细概述多种公平性公式,包括阿尔法公平性,比例公平性和集团平价性.
  • 确定各种优化建模技术的实际实施策略.
  • 对公理和讨价还价推导的调查,考虑人际实用性可比性.

结论:

  • 为优化提供了数学公平性标准的结构化概述.
  • 提供了关于选择和实施适当的公平性表述的指导.
  • 强调了决策模型中道德考虑的重要性.