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

Stereotype Content Model02:16

Stereotype Content Model

15.5K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.5K
Modeling with Differential Equations01:25

Modeling with Differential Equations

98
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
98
Chemical Equations03:10

Chemical Equations

82.3K
Chemical equations represent the identities and relative quantities of substances involved in a chemical reaction. The substances undergoing reaction are called reactants, and their formulas are placed on the left side of the equation. The substances generated by the reaction are called products, and their formulas are placed on the right side of the equation. Plus signs (+) separate individual reactant and product formulas, and an arrow (→) separates the reactant and product (left and right)...
82.3K
The Nernst Equation02:59

The Nernst Equation

47.3K
Nonstandard Reaction Conditions
The interconnection between standard cell potentials and various thermodynamic parameters such as the standard free energy change ΔG° and equilibrium constant K has been previously explored. For example, a redox reaction involving zinc(II) and tin(II) ions at 1 M concentration with Eºcell = +0.291 V and ΔG° = −56.2 kJ is spontaneous.
47.3K
Thermochemical Equations02:55

Thermochemical Equations

36.1K
For a chemical reaction (the system) carried out at constant pressure – with the only work done caused by expansion or contraction – the enthalpy of reaction (also called the heat of reaction, ΔHrxn) is equal to the heat exchanged with the surroundings (qp).
36.1K
Exponential Equations for Modeling Growth02:33

Exponential Equations for Modeling Growth

266
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
266

You might also read

Related Articles

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

Sort by
Same author

Stained DNA Dot Detection (SD<sup>3</sup>): An automated tool for quantifying fluorescent features along single stretched DNA molecules.

DNA repair·2025
Same author

Photophysical image analysis: Unsupervised probabilistic thresholding for images from electron-multiplying charge-coupled devices.

PloS one·2024
Same author

Sensing membrane voltage by reorientation of dipolar transmembrane peptides.

Biophysical journal·2024
Same author

Multilayer diffusion modeling and Coherent anti-Stokes Raman scattering microscopy for spatially resolved water diffusion measurements in human skin.

Journal of biophotonics·2022
Same author

A simple cut and stretch assay to detect antimicrobial resistance genes on bacterial plasmids by single-molecule fluorescence microscopy.

Scientific reports·2022
Same author

Objective comparison of methods to decode anomalous diffusion.

Nature communications·2021

Related Experiment Video

Updated: Feb 15, 2026

Spectrophotometric Determination of Phycobiliprotein Content in Cyanobacterium Synechocystis
08:07

Spectrophotometric Determination of Phycobiliprotein Content in Cyanobacterium Synechocystis

Published on: September 11, 2018

16.2K

Bayesian inference with information content model check for Langevin equations.

Jens Krog1, Michael A Lomholt1

  • 1MEMPHYS-Center for Biomembrane Physics, Department of Physics, Chemistry, and Pharmacy, University of Southern Denmark, 5230 Odense M, Denmark.

Physical Review. E
|January 20, 2018
PubMed
Summary

We present a new information content model check to enhance Bayesian data analysis for stochastic processes. This method improves parameter inference and model selection, especially for complex systems like Langevin equations.

More Related Videos

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity
15:04

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

Published on: May 5, 2009

25.8K
Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

11.2K

Related Experiment Videos

Last Updated: Feb 15, 2026

Spectrophotometric Determination of Phycobiliprotein Content in Cyanobacterium Synechocystis
08:07

Spectrophotometric Determination of Phycobiliprotein Content in Cyanobacterium Synechocystis

Published on: September 11, 2018

16.2K
A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity
15:04

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

Published on: May 5, 2009

25.8K
Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements
10:25

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

11.2K

Area of Science:

  • Physics
  • Statistics
  • Computational Science

Background:

  • Bayesian data analysis is a robust framework for parameter inference and model selection in stochastic processes.
  • Conventional methods can be limited in complex systems with factors like coordinate-dependent mobilities and measurement noise.

Purpose of the Study:

  • To introduce an information content model check as a goodness-of-fit measure.
  • To complement existing Bayesian analysis techniques.
  • To extend the Bayesian framework for analyzing complex stochastic systems.

Main Methods:

  • Development of an information content model check.
  • Application of the extended Bayesian framework to a system of Langevin equations.
  • Demonstration of the method's efficacy in the presence of coordinate-dependent mobilities and measurement noise.

Main Results:

  • The information content model check serves as a valuable goodness-of-fit tool.
  • The extended Bayesian framework successfully analyzes systems where traditional methods fail.
  • Effective parameter inference and model selection are achieved for the Langevin equation system.

Conclusions:

  • The proposed information content model check enhances Bayesian data analysis.
  • The extended Bayesian framework provides a powerful approach for complex stochastic processes.
  • This work offers a more comprehensive toolset for scientists analyzing noisy, complex data.