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

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

570
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...
570
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

79
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...
79
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

181
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,...
181
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

96
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...
96
Variability: Analysis01:11

Variability: Analysis

158
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
158

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

Pathways for enhancing service capability of primary healthcare institutions: a dynamic qualitative comparative analysis.

Frontiers in public health·2026
Same author

Target Trial Emulation of Vaccine Effectiveness in 5- to 17-years-olds with Prior SARS-CoV-2 Infection.

Nature communications·2026
Same author

Development of an Early-Phase Local Model for Pandemics Using Public Health Data: Application to the COVID-19 Pandemic.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same author

Long COVID associated with SARS-CoV-2 reinfection among children and adolescents in the omicron era (RECOVER-EHR): a retrospective cohort study.

The Lancet. Infectious diseases·2025
Same author

The Cox-Pólya-Gamma algorithm for flexible Bayesian inference of multilevel survival models.

Biometrics·2025
Same author

Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe).

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2025
Same journal

Probabilistic Joint and Individual Variation Explained (ProJIVE) for Data Integration.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
Same journal

fastkqr: A Fast Algorithm for Kernel Quantile Regression.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
Same journal

Empirical Bayes Covariance Decomposition, and a Solution to the Multiple Tuning Problem in Sparse PCA.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
Same journal

Joint Registration and Conformal Prediction for Partially Observed Functional Data.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
Same journal

Efficient Decision Trees for Tensor Regressions.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
Same journal

Distributed Nonparametric Regression with Heterogeneity Through Prediction-Based Aggregation.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
查看所有相关文章

相关实验视频

Updated: Jul 18, 2025

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

3.4K

使用变量函数混合模型进行超快的近似推理.

Shuning Huo1, Jeffrey S Morris2, Hongxiao Zhu1

  • 1Department of Statistics, Virginia Tech.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|August 23, 2023
PubMed
概括
此摘要是机器生成的。

我们开发了一个快速的计算框架来分析高维的功能数据. 这种方法使用变量贝叶斯来进行超快速的近似推断,克服传统贝叶斯函数混合模型的局限性.

关键词:
估计贝叶斯推理的推理分布式推理 分布式推理功能数据分析 功能数据分析并行计算是一种平行计算.变量贝叶斯是变量的贝叶斯.

更多相关视频

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

619
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K

相关实验视频

Last Updated: Jul 18, 2025

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

3.4K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

619
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K

科学领域:

  • 计算统计学 计算统计学
  • 功能数据分析 功能数据分析
  • 高维数据分析 高维数据分析

背景情况:

  • 贝叶斯函数混合模型对复杂的函数数据有效.
  • 后端采样中的计算挑战限制了它们对高维数据的应用.

研究的目的:

  • 引入一种新的计算框架,用于在高维函数数据中进行超快速近似推断.
  • 解决现有的贝叶斯函数混合模型的计算局限性.

主要方法:

  • 用于功能观测的节的基础表示.
  • 采用变量贝叶斯来近似后面分布,避免了计算密集的马尔科夫链蒙特卡洛 (MCMC) 采样.
  • 实现了一个用于参数估计的快速代算法,以及在基础空间中的快速多重测试程序.

主要成果:

  • 拟议的框架可以实现高效的压缩和并行计算.
  • 展示了超快速的近似推断能力.
  • 成功识别显著的本地区域,表明模拟研究和现实世界数据集 (蛋白质组学,脑部成像) 中的群体差异.

结论:

  • 新的框架为高维函数数据分析提供了计算效率高的解决方案.
  • 它为传统方法提供了可行的替代方案,使得推断更快,更具可扩展性.
  • 该方法通过模拟和各种科学领域的应用来验证.