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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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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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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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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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Poisson's And Laplace's Equation01:25

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The electric potential of the system can be calculated by relating it to the electric charge densities that give rise to the electric potential. The differential form of Gauss's law expresses the electric field's divergence in terms of the electric charge density.
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Linear time-invariant Systems01:23

Linear time-invariant Systems

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A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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对于多变量延迟微分方程模型的最大概率推理.

Ahmed Adly Mahmoud1, Abdalla Rabie1, Sarat Chandra Dass2

  • 1Department of Mathematics, Faculty of Science, Al-Azhar University, Assiut, 71524, Egypt.

Scientific reports
|July 12, 2025
PubMed
概括

为一般延迟微分方程模型开发了一个新的最大概率推理框架. 这种方法在没有限制性假设的情况下处理多个延迟参数,为复杂系统推进统计建模.

关键词:
延迟微分方程 (DDE) 是一种延迟微分方程.延迟微分方程模型 (DDEMs) 的使用.延迟的药理动力学模型延迟易感-感染-恢复 (SIR) 模型最大的概率估计 (MLE)

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科学领域:

  • 数学建模的数学建模
  • 统计推理 统计推理
  • 动态系统 动态系统

背景情况:

  • 延迟微分方程 (DDE) 对于模拟具有时间延迟的系统至关重要.
  • 之前的DDE推理方法经常对模型结构施加限制性假设.
  • 具有多个延迟的多变量DDEs带来了重大的推断挑战.

研究的目的:

  • 为多变量延迟微分方程模型开发一个灵活的最大概率推理框架.
  • 克服以前方法的局限性,不要假设DDEs的特定形式.
  • 为了使最大概率推理能够应用于更广泛的DDE模型类别.

主要方法:

  • 开发一个最大概率推断框架,用于一般的DDEs.
  • 实现自适应网格和梯度下降数值算法.
  • 制定估计信息矩阵和构建置信区间的方法.

主要成果:

  • 建立了一个强大的框架,用于在具有一个或多个延迟参数的多变量DDE中推断最大概率.
  • 数字算法 (自适应网格,梯度下降) 已开发用于参数估计和信息矩阵计算.
  • 该框架在流行病和药理动力学模型上进行了演示,显示了其实际适用性.

结论:

  • 开发的框架为分析DDE模拟的复杂系统提供了一个强大的工具.
  • 该方法的通用性允许在科学研究中更广泛地应用,包括流行病学和药理学.
  • 这项工作为一类重要的动态系统推进了统计推理能力.