Jove
Visualize
联系我们
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

相关概念视频

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

252
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
252
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

86
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...
86
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

149
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
149
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

710
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...
710
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

126
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...
126
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
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...
100

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

Reduced pulmonary function and increased pro-inflammatory cytokines in nanoscale carbon black-exposed workers.

Particle and fibre toxicology·2014
Same author

Single-layer transition metal dichalcogenide nanosheet-based nanosensors for rapid, sensitive, and multiplexed detection of DNA.

Advanced materials (Deerfield Beach, Fla.)·2014
Same author

The Arabidopsis ORGAN SIZE RELATED 2 is involved in regulation of cell expansion during organ growth.

BMC plant biology·2014
Same author

Localization and centrality in networks.

Physical review. E, Statistical, nonlinear, and soft matter physics·2014
Same author

Enhanced local bioavailability of single or compound drugs delivery to the inner ear through application of PLGA nanoparticles via round window administration.

International journal of nanomedicine·2014
Same author

Effects of mulching tolerant plant straw on soil surface on growth and cadmium accumulation of Galinsoga parviflora.

PloS one·2014

相关实验视频

Updated: Sep 10, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.0K

使用多变量试验模型对马尔科夫链蒙特卡洛的识别和收行为

Xiao Zhang1

  • 1Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.

Communications in statistics: theory and methods
|August 22, 2025
PubMed
概括

这项研究调查了参数扩展如何影响多变量试验模型中的马尔科夫链蒙特卡洛 (MCMC) 趋同. 它将可识别和不可识别模型之间的MCMC性能进行比较,为统计分析提供实用指导.

科学领域:

  • 统计数据
  • 经济计量学
  • 计算统计

背景情况:

  • 多变量试验模型用于分析多变量顺序数据.
  • 可识别的模型需要相关性矩阵,使统计分析复杂化.
  • 参数扩展会产生无法识别的模型,但其对MCMC的影响还未得到充分研究.

研究的目的:

  • 调查扩展参数对MCMC趋同的影响.
  • 将可识别和不可识别的多变量试验模型之间的MCMC性能进行比较.
  • 为构建不可识别模型和MCMC方法提供实用指导.

主要方法:

  • 模拟研究以评估MCMC的趋同和行为.
  • 对可识别与不可识别模型的MCMC算法的比较.
  • 应用到来自RLMS-HSE研究的现实数据.

主要成果:

  • 扩展的参数可以显著影响MCMC的趋同.
  • 在某些MCMC场景中,不可识别的模型可能具有优势.
  • 这项研究提供了对模型构建和采样方法开发的见解.

结论:

关键词:
可识别性美国多变量探针模型参数扩展

更多相关视频

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

3.4K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.1K

相关实验视频

Last Updated: Sep 10, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.0K
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

3.4K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.1K
  • 在多变量试验模型中,了解参数扩展效应对于高效的MCMC至关重要.
  • 这些发现为统计学家和数据分析师提供了实际指导.
  • 这项研究有助于对复杂的顺序数据进行可靠的统计分析.