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

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

394
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...
394
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

699
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,...
699
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
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
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

321
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...
321
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.3K

You might also read

Related Articles

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

Sort by
Same author

A Closer Look at Benchmarking Self-supervised Pre-training with Image Classification.

International journal of computer vision·2025
Same author

Mathematical discoveries from program search with large language models.

Nature·2023
Same author

Signal domain adaptation network for limited-view optoacoustic tomography.

Medical image analysis·2023
Same author

A benchmark dataset for machine learning in ecotoxicology.

Scientific data·2023
Same author

Regularizing transformers with deep probabilistic layers.

Neural networks : the official journal of the International Neural Network Society·2023
Same author

Comorbidity clusters associated with newly treated type 2 diabetes mellitus: a Bayesian nonparametric analysis.

Scientific reports·2022

Related Experiment Video

Updated: Mar 26, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

14.2K

Prior Design for Dependent Dirichlet Processes: An Application to Marathon Modeling.

Melanie F Pradier1,2, Francisco J R Ruiz1,2,3, Fernando Perez-Cruz1,2,4

  • 1Signal Theory and Communications Department, University Carlos III in Madrid, Madrid, Spain.

Plos One
|January 29, 2016
PubMed
Summary

This study introduces Bayesian nonparametrics (BNP) for marathon running analysis. The new methods offer fair runner comparisons and predict finishing times using running patterns.

More Related Videos

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
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.7K

Related Experiment Videos

Last Updated: Mar 26, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

14.2K
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
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.7K

Area of Science:

  • Statistics
  • Sports Science
  • Data Modeling

Background:

  • Current marathon grading systems lack fairness, often relying on limited top-tier data.
  • Understanding factors influencing runner performance and predicting race outcomes remains challenging.

Purpose of the Study:

  • To apply Bayesian nonparametrics (BNP) for comprehensive marathon data modeling.
  • To develop a fair grading system for marathon runners, accounting for age, gender, and environmental factors.
  • To analyze marathon running patterns for training insights and predictive modeling of finishing times.

Main Methods:

  • Utilized two Bayesian nonparametric priors: the single-p dependent Dirichlet process and the hierarchical Dirichlet process.
  • Developed a novel density comparison methodology for fair runner performance evaluation.
  • Analyzed temporal running patterns to extract training-relevant information and predict race outcomes.

Main Results:

  • A fair grading method was derived, enabling direct comparison of runners across different demographics.
  • Running patterns were analyzed, providing insights valuable for training strategies.
  • The models demonstrated the ability to predict marathon finishing times using intermediate measurements.

Conclusions:

  • Bayesian nonparametrics offer a robust framework for marathon data analysis.
  • The developed methods provide a more equitable approach to runner assessment and performance prediction.
  • The findings have implications for runner training, performance evaluation, and broader applications of density comparison.