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

Causality in Epidemiology01:21

Causality in Epidemiology

1.5K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.5K
Nursing Interventions I: Taxonomy of Nursing Interventions01:03

Nursing Interventions I: Taxonomy of Nursing Interventions

3.7K
Nursing interventions are chosen as part of the planning process to achieve patient outcomes. Once nursing diagnoses are determined, the goals and outcomes are specified, then the nursing interventions are selected and individualized according to the patient's situation.
A nursing intervention is a treatment or action based on scientific concepts and knowledge from the nursing, behavioral, and physical sciences. Identifying and prioritizing nursing interventions based on the desired outcome...
3.7K
Nursing Interventions II: Selecting and Classifying the Nursing Interventions01:29

Nursing Interventions II: Selecting and Classifying the Nursing Interventions

3.2K
Creating and executing a nursing diagnosis helps nurses plan care and guide patient, family, and community interventions. They are developed based on a patient's physical evaluation and support measuring the outcomes. It is not recommended to select random interventions throughout the planning process. Instead, consider the following six essential factors when choosing interventions:
3.2K
Incentive Theory: Pull Theory of Motivation01:18

Incentive Theory: Pull Theory of Motivation

917
Incentive theory, or the "pull theory" of motivation, suggests that external rewards primarily drive behavior. Individuals are motivated to engage in activities when they anticipate a desirable outcome. This is why people often work hard for promotions or study intensively to achieve high grades. These incentives can be tangible, physical rewards such as money or promotions, or intangible, non-physical rewards like praise and social recognition.
The theory differentiates between...
917
Group Design02:01

Group Design

10.4K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
10.4K
Bioavailability Study Design: Single Versus Multiple Dose Studies01:11

Bioavailability Study Design: Single Versus Multiple Dose Studies

228
Bioavailability studies are essential for understanding how a drug is absorbed, distributed, metabolized, and excreted in the body. These studies assess the extent and rate at which the active pharmaceutical agent becomes available at the site of action. The design of bioavailability studies can involve single-dose or multiple-dose regimens, each with distinct advantages and limitations.Single-dose studies are the preferred approach due to their simplicity and reduced drug exposure for...
228

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

Causal Graphical Models and Their Applications.

Entropy (Basel, Switzerland)·2026
Same author

Comparing Machine Learning Methods to Improve Fall Risk Detection in Elderly with Osteoporosis from Balance Data.

Journal of healthcare engineering·2021
查看所有相关文章

相关实验视频

Updated: Jan 29, 2026

Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design
07:40

Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design

Published on: May 31, 2021

3.8K

通过最佳干预来设计单阶段的因果激励机制.

Sebastián Bejos1, Eduardo F Morales1, Luis Enrique Sucar1

  • 1Computer Science Department, National Institute of Astrophysics, Optics and Electronics, Puebla 72840, Mexico.

Entropy (Basel, Switzerland)
|January 28, 2026
PubMed
概括

我们介绍了因果激励设计 (CID),这是一个使用因果推理用于私人信息的主要代理问题的框架. 这使得在复杂的系统中选择最佳的激励措施,以获得更好的决策.

关键词:
贝叶斯优化是贝叶斯的优化.斯过程是高斯过程.应用因果图形模型的应用.有关因果推理的推理.不同的差异.阶层化的斯塔克尔伯格游戏激励设计的激励设计获取信息获取信息主要代理问题遗憾分析 遗憾分析

更多相关视频

Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.6K
Optimized Ex-ovo Culturing of Chick Embryos to Advanced Stages of Development
05:47

Optimized Ex-ovo Culturing of Chick Embryos to Advanced Stages of Development

Published on: January 24, 2015

14.5K

相关实验视频

Last Updated: Jan 29, 2026

Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design
07:40

Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design

Published on: May 31, 2021

3.8K
Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.6K
Optimized Ex-ovo Culturing of Chick Embryos to Advanced Stages of Development
05:47

Optimized Ex-ovo Culturing of Chick Embryos to Advanced Stages of Development

Published on: January 24, 2015

14.5K

科学领域:

  • 经济学 经济学 经济学
  • 计算机科学 计算机科学
  • 因果推理因果推理
  • 机制设计 机制设计

背景情况:

  • 主要代理问题 (PAP) 涉及在双边私人信息下优化激励措施.
  • 现有的框架经常与私人信息和因果关系的复杂性作斗争.
  • 因果图形模型 (CGM) 提供了一种正式的方式来表示这些关系.

研究的目的:

  • 引入因果激励设计 (CID),这是一个新的框架,将因果推理与PAP整合在一起.
  • 通过使用CGM和模式激励作为干预措施来正式制定PAP.
  • 从观察数据中开发估计和选择最佳激励政策的方法.

主要方法:

  • 使用添加噪音CGM的正式PAP.
  • 模拟激励作为对函数空间变量 (Γ) 的干预措施.
  • 开发了一个功能因果贝叶斯优化 (FCBO) 算法用于政策选择,利用功能高斯过程和UCB获取函数.

主要成果:

  • 定义了一个因果估计和V ((Γ) 代表主体在干预下预期的效用.
  • 开发了使用高斯-赫米特方程和内核重权的高效估计技术.
  • 为FCBO算法建立了高概率的累积后悔边界.

结论:

  • 在单击游戏中,CID提供了一个因果决策管道,用于选择高性能激励措施.
  • 该框架允许离线政策选择,适用于适应性部署不切实际的场景.
  • 这项工作开创了CGM和因果推理用于激励设计和PAP的应用.