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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

74
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...
74
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

44
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...
44
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

107
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
107
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

109
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...
109
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

140
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
140
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

57
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...
57

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

A Latent Hidden Markov Model for Process Data.

Psychometrika·2026
Same author

Subtask analysis of process data through a predictive model.

The British journal of mathematical and statistical psychology·2022
Same author

An exploratory analysis of the latent structure of process data via action sequence autoencoders.

The British journal of mathematical and statistical psychology·2020
Same journal

Testing linear hypotheses in repeated measures generalized linear models using external information.

Psychometrika·2026
Same journal

When Do Unifactorial Items Increase the Reliability?

Psychometrika·2026
Same journal

Longitudinal Designs for Diagnostic Models: Identification and Estimation.

Psychometrika·2026
Same journal

Modeling Rare Events and Nonmonotone Nonignorable Missingness of Time-Varying Outcomes and Predictors in Binary Time-Series Daily Diary Data: A Bayesian Selection Model.

Psychometrika·2026
Same journal

Revelle's Beta: The Wait Is Over-Computation Becomes Possible.

Psychometrika·2026
Same journal

On dimensional implication graphs.

Psychometrika·2026
查看所有相关文章

相关实验视频

Updated: Jul 11, 2025

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.1K

一个潜在的隐藏的马尔科夫模型处理数据.

Xueying Tang1

  • 1University of Arizona, 617 N. Santa Rita Ave., Tucson, AZ , 85721, USA. xytang@math.arizona.edu.

Psychometrika
|November 7, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个新的统计模型,使用隐藏的马尔科夫模型来解释复杂的基于计算机的解决问题的数据. 该模型阐明了潜在特征中的个体差异如何影响不同的问题解决阶段,以更好地理解反应行为.

关键词:
隐藏的马尔科夫模型隐性变量是一个隐性变量.解决问题的行为 解决问题的行为响应过程 响应过程

更多相关视频

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

9.9K
Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research
08:12

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research

Published on: February 16, 2024

9.5K

相关实验视频

Last Updated: Jul 11, 2025

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.1K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

9.9K
Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research
08:12

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research

Published on: February 16, 2024

9.5K

科学领域:

  • 教育测量教育的测量
  • 心理测量 心理测量 心理测量
  • 认知科学 认知科学

背景情况:

  • 响应过程数据为解决问题的行为提供了洞察力.
  • 目前的特征提取方法缺乏对原始响应过程的解释性.
  • 了解问题解决中的受访者异质性至关重要.

研究的目的:

  • 提出一个统计模型来描述和分析基于计算机解决问题的响应过程.
  • 提供一种可解释的方法来描述问题解决策略中的个体差异.
  • 将隐藏的特征与可观察到的解决问题的阶段联系起来.

主要方法:

  • 使用隐藏的马尔科夫模型 (HMM) 来表示响应过程.
  • 将潜伏特征集成到HMM框架中,以解释受访者异质性.
  • 将模型应用于模拟实验和国际学生评估计划 (PISA) 过程数据.

主要成果:

  • 提出的基于HMM的模型成功地描述了响应过程及其在受访者之间的变化.
  • 纳入潜在特征可以提高模型的节性和可解释性.
  • 该模型有效地描述了问题解决阶段的异质性.

结论:

  • 统计模型为分析复杂的响应过程数据提供了一种强大而可解释的方法.
  • 这种方法促进了对解决问题的认知过程和个人差异的理解.
  • 这些发现对教育评估和基于计算机的测试环境的设计有影响.