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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

26
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...
26
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

14
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...
14
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

225
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...
225
Bootstrapping01:24

Bootstrapping

560
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
560
What are Estimates?01:06

What are Estimates?

4.9K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
4.9K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

45
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...
45

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

Federated learning: A new frontier in the exploration of multi-institutional medical imaging data.

Computer methods and programs in biomedicine·2026
Same author

Protocol for automated analysis of biological images using Python code.

STAR protocols·2026
Same author

Graph Attention Networks for Detecting Epilepsy From EEG Signals Using Accessible Hardware in Low-Resource Settings.

IEEE open journal of engineering in medicine and biology·2026
Same author

Introducing QuantConn: Overcoming challenging diffusion acquisitions with harmonization.

Computational diffusion MRI. CDMRI (Workshop)·2025
Same author

Modeling the Spread of Misfolded Proteins in Alzheimer's Disease using Higher-Order Simplicial Complex Contagion.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Editorial: Spatiotemporal & AI trends in neuroscience, neuroimaging, and neurooncology.

Frontiers in neuroimaging·2025
Same journal

MT-MRI for detection of renal interstitial fibrosis in renovascular disease.

Scientific reports·2026
Same journal

Detection of underground objects from GPR data using a lightweight YOLO-based approach.

Scientific reports·2026
Same journal

Early systemic inflammatory-metabolic trajectory phenotypes are associated with survival outcomes in metastatic renal cell carcinoma treated with nivolumab.

Scientific reports·2026
Same journal

Water balance components in a dry-seeded rice-wheat system: Untangling the effects of tillage and mulching practices.

Scientific reports·2026
Same journal

Topological approaches to quantum tensor train compression via ZX-calculus and SVD.

Scientific reports·2026
Same journal

determinants of flood impacts and adaptive capacity among market vendors in Walukuba-Masese, Jinja city, Uganda.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: May 10, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.8K

从机器学习模型中通过反向估计和贝叶斯推理重建数据.

Agus Hartoyo1,2, Dominika Ciupek3, Maciej Malawski3,4

  • 1Sano Centre for Computational Medicine, Kraków, Poland. a.hartoyo@sanoscience.org.

Scientific reports
|April 22, 2025
PubMed
概括
此摘要是机器生成的。

研究人员可以使用反向估计从训练有素的机器学习模型中重建原始数据集. 数据重建的质量取决于先前的准确性和模型的准确性,使得合成模型的创建.

更多相关视频

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

3.3K
A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

35.2K

相关实验视频

Last Updated: May 10, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

10.8K
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

3.3K
A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

35.2K

科学领域:

  • 机器学习 机器学习
  • 贝叶斯的推理是贝叶斯的推理.
  • 数据科学数据科学数据科学

背景情况:

  • 机器学习模型经常保留有关训练数据的信息.
  • 恢复这些数据对于隐私,安全和模型理解至关重要.
  • 目前用于数据重建的方法在理论基础上是有限的.

研究的目的:

  • 开发一个理论框架来理解从机器学习模型的数据重建.
  • 确定影响重建数据准确性的关键因素.
  • 为了能够创建模拟原始模型性能的合成模型.

主要方法:

  • 使用反向估计和贝叶斯推理来重建数据.
  • 开发了一个基于部分导数的新理论框架.
  • 量化了先前精度和模型精度对重建质量的影响.

主要成果:

  • 衍生表达式,将变量变化与后方分歧联系起来.
  • 确定数据重建的准确性取决于先前和模型的准确性.
  • 对基准数据集的实证结果验证了理论框架.

结论:

  • 理论框架提供了对数据重建的强有力的理解.
  • 准确的先验和机器学习模型对于高保真数据恢复至关重要.
  • 该方法促进了各种应用的有效合成模型的生成.