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

Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

4.1K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
4.1K
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

940
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...
940
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.8K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.8K
Biostatistics: Overview01:20

Biostatistics: Overview

541
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
541
Binomial Probability Distribution01:15

Binomial Probability Distribution

14.7K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
14.7K
Quadratic Models01:23

Quadratic Models

76
Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
76

You might also read

Related Articles

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

Sort by
Same author

Oxidative stress-driven transcriptomic remodeling in human astrocytes reveals network signatures associated with neurodegenerative and cardiovascular processes.

Computational and structural biotechnology journal·2026
Same author

Higher-order interactions in neuronal function: From genes to ionic currents in biophysical models.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

A general framework for interpretable neural learning based on local information-theoretic goal functions.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Shared Proteins and Pathways of Cardiovascular and Cognitive Diseases: Relation to Vascular Cognitive Impairment.

Journal of proteome research·2024
Same author

What happens in the brain when we die? Deciphering the neurophysiology of the final moments in life.

Frontiers in aging neuroscience·2023
Same author

Quantifying Reinforcement-Learning Agent's Autonomy, Reliance on Memory and Internalisation of the Environment.

Entropy (Basel, Switzerland)·2022
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Nov 27, 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

3.6K

BROJA-2PID: A Robust Estimator for Bivariate Partial Information Decomposition.

Abdullah Makkeh1, Dirk Oliver Theis1, Raul Vicente1

  • 1Institute of Computer Science, University of Tartu, Ülikooli 17, 51014 Tartu, Estonia.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

A new software robustly computes the Bertschinger et al. partial information decomposition (BROJA PID) measure using a Cone Programming model. This study proves key mathematical properties and demonstrates the software

Keywords:
Cone Programmingbivariate information decomposition

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.0K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.6K

Related Experiment Videos

Last Updated: Nov 27, 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

3.6K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.0K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.6K

Area of Science:

  • Information Theory
  • Computational Neuroscience
  • Machine Learning

Background:

  • Partial Information Decomposition (PID) quantifies information shared among multiple variables.
  • The Bertschinger et al. PID (BROJA PID) measure is theoretically significant but computationally challenging.
  • Previous computational approaches lacked robustness and scalability.

Purpose of the Study:

  • To develop and validate a robust computational tool for the BROJA PID measure.
  • To establish the theoretical underpinnings of the Cone Programming approach for PID.
  • To provide a practical software implementation for researchers.

Main Methods:

  • Utilized a Cone Programming model, identified as the most robust method for BROJA PID computation.
  • Developed production-quality software implementing the Cone Programming approach.
  • Proved strong duality for the Cone Program and its equivalence to the original convex problem.

Main Results:

  • The developed software accurately and robustly computes the BROJA PID measure.
  • Experimental comparisons demonstrate superior performance over existing estimators.
  • The software framework shows potential for extension to trivariate PID measures.

Conclusions:

  • The Cone Programming model provides a robust and theoretically sound foundation for computing BROJA PID.
  • The released software offers a practical and efficient solution for researchers in information theory and related fields.
  • This work advances the computational capabilities for analyzing complex information structures.