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

BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

766
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
766
Probability Laws01:49

Probability Laws

43.2K
Overview
43.2K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.1K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.1K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

214
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
214
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.5K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.5K
Classification of Systems-I01:26

Classification of Systems-I

455
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
455

You might also read

Related Articles

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

Sort by
Same author

Expectation Identities for Dynamical Systems: A Classical Analog of the Ehrenfest Theorem.

Entropy (Basel, Switzerland)·2026
Same author

DNA damage and cell death induced by exposure to ultra-high dose rate low-dose pulsed X-rays emitted from a kilojoule plasma focus device.

Biological research·2026
Same author

Monte Carlo optimization for sampling selection in imbalanced data applied to student dropout prediction.

Chaos (Woodbury, N.Y.)·2025
Same author

Kappa distribution from particle correlations in nonequilibrium, steady-state plasmas.

Physical review. E·2024
Same author

Design, implementation, and preliminary in-vivo assessment of a high-CMRR low-NEF wireless EEG miniaturized platform.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2023
Same author

Evaluating the Adiabatic Invariants in Magnetized Plasmas Using a Classical Ehrenfest Theorem.

Entropy (Basel, Switzerland)·2023

Related Experiment Video

Updated: Nov 27, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.5K

Probabilistic Inference for Dynamical Systems.

Sergio Davis1,2, Diego González1,3, Gonzalo Gutiérrez3

  • 1Comisión Chilena de Energía Nuclear, Casilla 188-D Santiago, Chile.

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

This study introduces a general framework for dynamical systems inference using Bayesian probability and maximum entropy. It naturally derives fluid dynamics equations from path concepts, incorporating system specifics via maximum path entropy.

Keywords:
bayesian inferencedynamical systemsfluid equations

More Related Videos

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

8.8K
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

2.8K

Related Experiment Videos

Last Updated: Nov 27, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.5K
An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

8.8K
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

2.8K

Area of Science:

  • Dynamical Systems and Statistical Physics
  • Bayesian Inference and Information Theory

Background:

  • Dynamical systems modeling often relies on specialized equations.
  • Integrating diverse information into these models can be challenging.
  • A unified probabilistic framework is needed for robust inference.

Purpose of the Study:

  • To present a general framework for inference in dynamical systems.
  • To demonstrate the natural emergence of established physical laws within this framework.
  • To provide a method for incorporating specific system information using information-theoretic principles.

Main Methods:

  • Utilizing Bayesian probability theory as the foundational language.
  • Employing the maximum entropy principle for inference.
  • Defining system dynamics based on the fundamental concept of a 'path'.
  • Applying the maximum caliber (maximum path entropy) principle to include specific system information.

Main Results:

  • The framework naturally derives the continuity and Cauchy equations of fluid dynamics.
  • The approach provides a principled way to incorporate system-specific data.
  • It establishes a connection between path integrals and fundamental physical laws.

Conclusions:

  • The proposed Bayesian framework offers a unified approach to dynamical systems inference.
  • Maximum entropy and maximum caliber principles are powerful tools for deriving physical laws and incorporating data.
  • This method enhances the understanding and modeling of complex dynamical systems.