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

497
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,...
497
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

241
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...
241
Econometric Views (EViews)01:29

Econometric Views (EViews)

544
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
544
Three-Compartment Open Model01:06

Three-Compartment Open Model

834
The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...
834
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

1.1K
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...
1.1K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

282
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...
282

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

Building bridges between brain and behavior: An open-source toolbox for joint modeling with fMRI.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Positive Bias in Value-Based Decision Making: Neurocognitive Associations with Resilience.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026
Same author

Effector-specific corticospinal modulation is preserved in older adults during proactive stopping: A novel Bayesian approach.

Neurobiology of aging·2026
Same author

An illustrative guide to expressing cognitive theories using evidence accumulation modelling.

Behavior research methods·2026
Same author

Joint Cognitive Models Reveal Sources of Robust Individual Differences in Conflict Processing.

Computational brain & behavior·2026
Same author

The diffusion model's drift rate parameter primarily reflects efficiency, rather than speed, of evidence accumulation.

Psychonomic bulletin & review·2026

相关实验视频

Updated: Jan 14, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

586

使用EMC2包的贝叶斯层次认知建模.

Niek Stevenson1, Michelle C Donzallaz2, Reilly J Innes2

  • 1Department of Psychology, University of Amsterdam, Amsterdam, Netherlands. niek.stevenson@gmail.com.

Behavior research methods
|January 12, 2026
PubMed
概括
此摘要是机器生成的。

本研究介绍了EMC2,这是一个R包,用于对认知选择模型的贝叶斯层次分析. 它简化了模型规范,估计,批评和推断,增强了认知建模工作流程.

关键词:
认知模型 认知模型证据积累模型的模型.层次化的贝叶斯学在R包中,R包是R包.

更多相关视频

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.2K
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

3.7K

相关实验视频

Last Updated: Jan 14, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

586
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.2K
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

3.7K

科学领域:

  • 认知科学 认知科学
  • 计算神经科学是一种神经科学.
  • 贝叶斯统计学贝叶斯统计学

背景情况:

  • 选择的认知模型对于理解决策至关重要.
  • 贝叶斯层次分析为这些模型提供了一个强大的框架.
  • 现有的工作流可以是复杂和计算密集的.

研究的目的:

  • 介绍EMC2,一个新的R包用于对认知模型的贝叶斯层次分析.
  • 提供全面的五阶段工作流程,简化认知模型分析.
  • 为了促进复杂的认知模型的规范,估计,批评和推断.

主要方法:

  • 开发EMC2 R包,使用五阶段工作流程.
  • 对认知模型参数的线性模型规范的整合.
  • 实施灵活的先验,层次结构和高效的抽样算法.
  • 包括用于模型批评和推理的函数.

主要成果:

  • 对于计算密集型认知模型,EMC2提供了一个用户友好的界面.
  • 该包桥梁标准回归和认知建模.
  • 使用两个证据积累模型证明工作流程.

结论:

  • EMC2显著地简化并指导贝叶斯层次认知模型的分析.
  • 该套件支持模型评估,改进,比较和解释.
  • EMC2提高了先进的认知建模技术的可访问性和效率.