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相关概念视频

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

32
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...
32
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
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...
45

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使用后期预测模拟来评估形态模型的充分性.

Laura P A Mulvey1, Michael R May2, Jeremy M Brown3

  • 1GeoZentrum Nordbayern, Department of Geography and Geosciences, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Loewenichstraße 28, 91054 Erlangen, Germany.

Systematic biology
|October 7, 2024
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概括

选择合适的形态进化模型对于准确的家族遗传树来说至关重要. 这项研究表明,没有一个单一的模型适合所有人,强调仔细选择以获得更好的进化见解.

关键词:
贝叶斯的遗传学分析.模型的充分性模型的充分性模型选择,模型选择.形态数据 形态数据形态模型的形态模型.考古生物学的古生物学

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科学领域:

  • 进化生物学是进化的生物学.
  • 系统学 系统学 系统学
  • 计算生物学是一种计算生物学.

背景情况:

  • 遗传树对于理解生命的进化历史和多样化至关重要.
  • 形态学数据与分子数据一起,对遗传学至关重要,特别是用于结合化石种类,并将灭绝的和现存的物种结合起来.
  • 马克·易斯模型通常用于形态特征进化,但其充分性尚未完全理解.

研究的目的:

  • 用四足动物数据集调查不同形态模型对遗传学估计的影响.
  • 使用后期预测模拟来评估模型的充分性,并与贝叶斯模型选择进行比较.
  • 确定当前的Mk模型变异是否足够描述形态演变,以及是否有任何扩展是普遍可取的.

主要方法:

  • 将未分区的Mk模型与分区模型 (按观察到的状态) 进行比较,有或没有速率变化和确定偏差校正.
  • 利用后期预测模拟来评估模型的充分性.
  • 将模型充分性与贝叶斯模型选择进行比较,特别是解决不同字符状态空间的限制.

主要成果:

  • 替代模型的选择显著影响了家族遗传树拓和分支长度.
  • 随后的预测模拟被验证为在这种情况下选择模型的合适方法.
  • 基于边际概率的模型选择对于具有不同字符状态空间的模型是不合适的.
  • 目前的Mk模型变体通常表现良好,但没有一个扩展显示跨数据集的普遍偏好.

结论:

  • 在贝叶斯系遗传学中,模型选择对于形态数据至关重要,因为没有"一刀切"的解决方案.
  • 仔细考虑离散的特征进化模型可以增强对已灭绝和现存类型的类遗传学估计的信心.
  • 后期预测模拟是评估遗传学模型充分性的强有力的方法.