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

298
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...
298
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

305
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...
305
Reversible and Irreversible Processes01:14

Reversible and Irreversible Processes

6.0K
The thermodynamic processes can be classified into reversible and irreversible processes. The processes that can be restored to their initial state are called reversible processes. It is only possible if the process is in quasi-static equilibrium, i.e., it takes place in infinitesimally small steps, and the system remains at equilibrium However, these are ideal processes and do not occur naturally. An ideal system undergoing a reversible process is always in thermodynamic equilibrium within...
6.0K
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

3.3K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
3.3K
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

649
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...
649
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

441
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
441

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·2023
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: Feb 26, 2026

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

744

一个潜在的隐藏的马尔科夫模型处理数据.

Xueying Tang1

  • 1University of Arizona.

Psychometrika
|February 25, 2026
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来解释复杂的基于计算机的解决问题的数据. 该模型使用隐藏的马尔科夫模型来了解个人差异如何影响问题解决过程,为受访者行为提供了更清晰的见解.

关键词:
隐藏的马尔科夫模型隐藏变量的潜伏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

10.4K
Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

336

相关实验视频

Last Updated: Feb 26, 2026

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

744
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

10.4K
Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

336

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:

  • 来自基于计算机的评估的响应过程数据 (RPD) 为解决问题的行为提供了洞察力.
  • 当前的数据驱动特征提取方法产生可解释的特征,但缺乏与原始响应过程的明确联系.
  • 这种差距阻碍了对潜在特征如何影响问题解决策略的深入理解.

研究的目的:

  • 提出一种用于分析响应过程数据的新型统计模型.
  • 为了提高从非结构化过程数据中提取的特征的可解释性.
  • 模拟受访者之间解决问题过程的异质性.

主要方法:

  • 开发了一个统计模型,将隐藏的特征与隐藏的马尔科夫模型 (HMMs) 集成在一起.
  • HMM结构代表了解决问题的阶段,隐藏状态作为子任务.
  • 隐藏的特征被纳入来解释响应过程中的变化.

主要成果:

  • 拟议的模型为RPD分析提供了一个节和可解释的框架.
  • 通过模拟研究证明了模型的有效性.
  • 使用来自国际学生评估计划 (PISA) 的现实世界数据验证了模型.

结论:

  • 隐藏的特征告知HMM提供了一个强大的工具,以了解个体在解决问题的差异.
  • 这种方法弥合了复杂的过程数据和可解释的心理结构之间的差距.
  • 促进在教育评估中对认知过程进行更细致的分析.