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Related Concept Videos

Susceptibility, Permittivity and Dielectric Constant01:26

Susceptibility, Permittivity and Dielectric Constant

When placed in an external electric field, a dielectric material gets polarized. The charge density in the dielectric material is given by the sum of the bound and free charge densities, while the total charge density can also be written in terms of the total electric field. The bound charge density can be measured in terms of polarization, leading to the relationship between electric displacement and polarization.
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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 particular...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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...
Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Propagation of Waves01:07

Propagation of Waves

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Consider a scenario where a wave propagates from a string of low linear mass density to a string of high linear mass density. In such a case, the reflected wave is out of phase with respect to the incident wave, however the...
Thevinin's Theorem01:15

Thevinin's Theorem

Thévenin's theorem plays a pivotal role in electrical circuit analysis, offering a solution to the challenges posed by variable loads within a circuit. In practical applications, it is common to encounter circuits where certain elements remain fixed while others fluctuate, often referred to as the "load." A typical household electrical outlet serves as a prime example of a variable load, as it can be connected to a variety of appliances, each with its own unique electrical characteristics.

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Related Experiment Video

Updated: May 14, 2026

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

Susceptibility propagation by using diagonal consistency.

Muneki Yasuda1, Kazuyuki Tanaka

  • 1Graduate School of Information Sciences, Tohoku University, Japan. muneki@smapip.is.tohoku.ac.jp

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 16, 2013
PubMed
Summary

This study introduces an improved susceptibility propagation method for approximating Markov random fields. The new approach enhances accuracy and robustness across diverse network structures.

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Last Updated: May 14, 2026

Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

Area of Science:

  • Statistical Physics
  • Computational Neuroscience
  • Machine Learning

Background:

  • Markov random fields are widely used for modeling complex systems.
  • Approximate computation methods are essential for analyzing large-scale Markov random fields.
  • Existing methods like belief propagation and linear response have limitations.

Purpose of the Study:

  • To develop an improved susceptibility propagation method for approximate computation of Markov random fields.
  • To enhance the robustness and accuracy of susceptibility propagation.
  • To provide a method that unifies existing approaches in special cases.

Main Methods:

  • Formulation of an improved susceptibility propagation by integrating diagonal matching with mean-field approaches.
  • Application of the method to inverse Ising problems.
  • Analysis of the method's performance on various network structures.

Main Results:

  • The proposed susceptibility propagation demonstrates robustness across different network structures.
  • The method achieves approximate computation for Markov random fields.
  • In specific cases, the improved method reduces to ordinary susceptibility propagation and the adaptive Thouless-Anderson-Palmer equation.

Conclusions:

  • The improved susceptibility propagation offers a more robust and versatile tool for analyzing Markov random fields.
  • This work provides a unified framework that connects different approximation techniques.
  • The findings have implications for statistical physics, machine learning, and computational neuroscience.