Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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

1.4K
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...
1.4K
Time-Series Graph00:54

Time-Series Graph

5.5K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.5K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

460
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
460
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

310
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...
310
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

697
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,...
697
Multiple Bar Graph01:07

Multiple Bar Graph

10.5K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
10.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

NeMO Analytics: a compendium of transcriptomic data for the exploration of neocortical development.

Nature neuroscience·2026
Same author

GAMing the Brain: Investigating the Cross-modal Relationships between Functional Connectivity and Structural Features using Generalized Additive Models.

Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco)·2026
Same author

Corrigendum to 'Brief parent-report measure of slowness in eating is associated with weight status in children with cystic fibrosis over a 3-year follow-up', Physiology & Behavior 2025 115104.

Physiology & behavior·2026
Same author

Shortcomings of deep learning for distributional predictors: a note.

Biostatistics (Oxford, England)·2026
Same author

Baseline Functional Connectivity Predicts Who Will Benefit From Neuromodulation: Evidence From Primary Progressive Aphasia.

Neurorehabilitation and neural repair·2026
Same author

Brief parent-report measure of slowness in eating is associated with weight status in children with cystic fibrosis over a 3-year follow-up.

Physiology & behavior·2025

Related Experiment Video

Updated: Mar 25, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.6K

Joint Estimation of Multiple Graphical Models from High Dimensional Time Series.

Huitong Qiu1, Fang Han1, Han Liu2

  • 1Johns Hopkins University, Baltimore, USA.

Journal of the Royal Statistical Society. Series B, Statistical Methodology
|March 1, 2016
PubMed
Summary

This study introduces a kernel-based method for estimating multiple graphical models from subject data with smooth variations. The approach effectively borrows information across individuals, improving parameter estimation accuracy.

Keywords:
Conditional independenceGraphical modelHigh dimensional dataRate of convergenceTime series

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.0K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Related Experiment Videos

Last Updated: Mar 25, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.6K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.0K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Area of Science:

  • Statistics
  • Machine Learning
  • Neuroimaging Analysis

Background:

  • Estimating graphical models is crucial for understanding complex systems.
  • High-dimensional data from multiple subjects presents unique statistical challenges.
  • Existing methods often struggle with smoothly varying relationships across subjects.

Purpose of the Study:

  • To develop a novel kernel-based method for jointly estimating multiple graphical models.
  • To address the challenge of smoothly changing graphical structures across subjects.
  • To provide theoretical guarantees and empirical validation for the proposed method.

Main Methods:

  • Kernel-based estimation framework for simultaneous graphical model inference.
  • Utilizing subject closeness to model smooth transitions in graphical structures.
  • Theoretical analysis under a double asymptotic framework (T, n, and dimension d increasing).

Main Results:

  • Explicit rates of convergence for parameter estimation are derived.
  • Quantification of information borrowing strength across subjects.
  • Demonstration of the method's effectiveness on synthetic and real rs-fMRI data.

Conclusions:

  • The proposed kernel-based method enables effective joint estimation of multiple graphical models.
  • The framework accurately captures smoothly varying relationships in high-dimensional data.
  • The method shows promise for applications in neuroimaging and other fields requiring analysis of interconnected systems.