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

Time-Series Graph00:54

Time-Series Graph

5.1K
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...
5.1K
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

680
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
680
Causality in Epidemiology01:21

Causality in Epidemiology

1.5K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.5K
Shape and Texture of Coarse Aggregate01:25

Shape and Texture of Coarse Aggregate

681
Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
681
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

740
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
740
What are Estimates?01:06

What are Estimates?

8.8K
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...
8.8K

You might also read

Related Articles

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

Sort by
Same author

Identifying the net information flow direction in mutually coupled non-identical chaotic oscillators.

Chaos (Woodbury, N.Y.)·2026
Same author

The Causal Interaction between Complex Subsystems.

Entropy (Basel, Switzerland)·2022
Same author

Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction.

Entropy (Basel, Switzerland)·2021
Same author

A Note on Causation versus Correlation in an Extreme Situation.

Entropy (Basel, Switzerland)·2021
Same author

A Study of the Cross-Scale Causation and Information Flow in a Stormy Model Mid-Latitude Atmosphere.

Entropy (Basel, Switzerland)·2020

Related Experiment Video

Updated: Jan 29, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.6K

Estimation of Information Flow-Based Causality with Coarsely Sampled Time Series.

X San Liang1,2

  • 1Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200438, China.

Entropy (Basel, Switzerland)
|January 28, 2026
PubMed
Summary

Information flow-based causality analysis struggles with coarsely sampled data in nonlinear systems. This study introduces a new method using Lie groups, improving accuracy for scarce observations, especially in synchronized systems.

Keywords:
Frobenius-Perron operatorLie groupRössler systemcoarsely sampled time seriesinformation flowquantitative causalitysynchronization

More Related Videos

Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course
11:33

Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course

Published on: July 18, 2014

43.9K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

947

Related Experiment Videos

Last Updated: Jan 29, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.6K
Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course
11:33

Transcranial Magnetic Stimulation for Investigating Causal Brain-behavioral Relationships and their Time Course

Published on: July 18, 2014

43.9K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

947

Area of Science:

  • Dynamical Systems
  • Information Theory
  • Causality Analysis

Background:

  • Information flow-based causality analysis is increasingly used, with a concise maximum likelihood estimator formula.
  • Current estimation algorithms based on differential dynamical systems face challenges with coarsely sampled time series, particularly for nonlinear systems.

Purpose of the Study:

  • To address the limitations of current causality analysis methods with coarsely sampled time series.
  • To develop a more robust causality analysis technique applicable to scarce observational data.

Main Methods:

  • The study proposes a novel approach by utilizing Lie groups instead of Lie algebras for causality analysis.
  • An explicit formula is derived using only sample covariances, suitable for coarsely sampled data.

Main Results:

  • The new method demonstrates qualitative suitability for linear systems and significantly reduces bias in nonlinear systems with reduced sampling frequency.
  • The approach was successfully applied to a system of coupled Rössler oscillators, showing remarkable performance even when oscillators are nearly synchronized.

Conclusions:

  • This research offers a partial but timely solution for conducting faithful causality analysis on coarsely sampled time series.
  • The Lie group-based method enhances the applicability of causality analysis in scenarios with limited observational data, such as in synchronized dynamical systems.