Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

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

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
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
Same journal

Planned missingness in intensive longitudinal studies: Extensions and comparisons of multiform designs.

Behavior research methods·2026
Same journal

A validity-guided workflow for robust large language model research in psychology.

Behavior research methods·2026
Same journal

Are 7-point Likert scales preferable to 5-point scales in language research?

Behavior research methods·2026
Same journal

Generative psychometrics via AI-GENIE: Automatic item generation and validation with network-integrated evaluation.

Behavior research methods·2026
Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026
Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026
関連記事をすべて見る

関連する実験動画

Updated: Jan 14, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

586

EMC2パッケージを用いた階層ベイズ認知モデリング

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
まとめ
この要約は機械生成です。

本研究では、認知選択モデルの階層ベイズ解析のためのRパッケージであるEMC2を紹介します。モデルの仕様、推定、評価、推論を合理化し、認知モデリングのワークフローを強化します。

キーワード:
認知モデル証拠蓄積モデル階層ベイズRパッケージ

さらに関連する動画

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

関連する実験動画

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

科学分野:

  • 認知科学
  • 計算神経科学
  • ベイズ統計学

背景:

  • 選択の認知モデルは、意思決定を理解するために不可欠です。
  • 階層ベイズ解析は、これらのモデルのための強力なフレームワークを提供します。
  • 既存のワークフローは、複雑で計算集約的になる可能性があります。

研究 の 目的:

  • 認知モデルの階層ベイズ解析のための新しいRパッケージであるEMC2を紹介すること。
  • 認知モデル解析を簡略化する包括的な5段階ワークフローを提供すること。
  • 複雑な認知モデルの仕様、推定、評価、推論を容易にすること。

主な方法:

  • 5段階ワークフローを備えたEMC2 Rパッケージの開発。
  • 認知モデルパラメータの線形モデル仕様の統合。
  • 柔軟な事前分布、階層構造、効率的なサンプリングアルゴリズムの実装。
  • モデル評価および推論のための関数の組み込み。

主要な成果:

  • EMC2は、計算集約的な認知モデルのためのユーザーフレンドリーなインターフェースを提供します。
  • このパッケージは、標準的な回帰と認知モデリングを橋渡しします。
  • 2つの証拠蓄積モデルを使用したワークフローを実証しました。

結論:

  • EMC2は、階層ベイズ認知モデルの解析を大幅に容易にし、ガイドします。
  • このパッケージは、モデルの評価、改良、比較、解釈をサポートします。
  • EMC2は、高度な認知モデリング技術へのアクセス性と効率性を向上させます。