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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.
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
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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:
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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.
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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偶然受限的多选项笔记本袋问题:模型,算法和应用

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    此摘要是机器生成的。

    本研究引入了一个新的机会受限制的多选项背包问题 (CCMCKP),用于未知重量分布的真实场景. 一个新的数据驱动自适应本地搜索 (DDALS) 算法有效地使用仅样本数据解决CCMCKP.

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    科学领域:

    • 运营研究 运营研究
    • 组合优化的优化.
    • 数据驱动优化数据驱动优化

    背景情况:

    • 多选项背包问题 (MCKP) 是一个众所周知的NP-hard问题,具有实际应用.
    • 一种新的变体,机会受约束的MCKP (CCMCKP),解决了项目权重是随机变量的场景.
    • 当概率分布未知并且只有样本数据可用时,现有方法会遇到困难.

    研究的目的:

    • 在数据驱动条件下,制定和解决机会受限制的多选项背包问题 (CCMCKP).
    • 开发一种新的算法,不需要对重量分布的先验知识.
    • 建立用于评估CCMCKP解决方案的基准数据集.

    主要方法:

    • 对于未知概率分布的 CCMCKP 的问题制定.
    • 开发一个数据驱动的自适应本地搜索 (DDALS) 算法.
    • 创建基于电信的合成和现实世界的基准实例.

    主要成果:

    • 拟议的DDALS算法在与现有的随机和分布强大的优化方法相比,表现优越.
    • 即使具有高机会约束强度和有限的样本数据,DDALS也有效.
    • 实验结果验证了DDALS在合成和应用特定基准上的有效性.

    结论:

    • 在没有分布假设的情况下,DDALS提供了一种可靠和有效的数据驱动方法来解决CCMCKP.
    • 开发的基准和DDALS算法为该领域的未来研究提供了基础.
    • 这项工作促进了背包问题变体在不确定的环境中的实际应用.