Jove
Visualize
联系我们

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

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

33
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...
33
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

432
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...
432
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

390
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
390
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

64
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...
64
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

46
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
46

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Automatic selection of the best neural architecture for time series forecasting.

Nature communications·2026
Same author

A variational framework for residual-based adaptivity in neural PDE solvers and operator learning.

NPJ artificial intelligence·2026
Same author

Safe Physics-Informed Machine Learning for Optimal Predefined-Time Stabilization: A Lyapunov-Based Approach.

IEEE transactions on neural networks and learning systems·2025
Same author

Accelerating gradient descent and Adam via fractional gradients.

Neural networks : the official journal of the International Neural Network Society·2023
Same author

Periodic Artifact Removal With Applications to Deep Brain Stimulation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2022
Same author

SAR image regularization with fast approximate discrete minimization.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2009
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jun 14, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.3K

对大规模,非平滑的最大值模型的高效第一阶算法,适用于野火科学

Gabriel Provencher Langlois1, Jatan Buch2, Jérôme Darbon3

  • 1Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA.

Entropy (Basel, Switzerland)
|August 29, 2024
PubMed
概括

新的算法有效地训练大数据的大规模,非平滑的最大 (MaxEnt) 模型. 这些方法改进了现有技术,为复杂的统计建模提供了更快的融合和可靠的结果.

关键词:
库尔巴克莱布勒的分歧.最大值估计的最大值.原始的双重方法.野火科学 野火科学 野火科学

更多相关视频

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.9K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

相关实验视频

Last Updated: Jun 14, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.3K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.9K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

科学领域:

  • 统计建模 统计建模
  • 机器学习 机器学习
  • 计算统计学 计算统计学

背景情况:

  • 最大 (MaxEnt) 模型对于从数据中估计概率分布至关重要.
  • 当前的优化算法与现代大数据集的规模和非平滑性作斗争.
  • 现有的方法可能会产生不可靠的结果,或适用于大规模应用的规模不佳.

研究的目的:

  • 开发新的优化算法,以高效地训练大规模,非光滑的MaxEnt模型.
  • 在大数据场景中克服最先进算法的局限性.
  • 为了提高MaxEnt模型培训的可扩展性和数值稳定性.

主要方法:

  • 提出了利用Kullback-Leibler分歧的新型第一阶段优化算法.
  • 为大规模,非光滑的MaxEnt模型设计的算法.
  • 证明了并行可行性和高效的步骤大小参数估计 (O(mn) 操作).

主要成果:

  • 算法实现了对大规模,非光滑的MaxEnt模型的高效训练.
  • 证明了卓越的性能,在数量级上超过了最先进的方法.
  • 在真实世界野火发生数据集上验证,显示与物理模型一致.

结论:

  • 这些新的算法为训练大数据上的复杂MaxEnt模型提供了高效和可扩展的解决方案.
  • 这些方法提供了更好的收率和数字可靠性.
  • 这种方法对于生态建模和野火预测等应用非常有效.