Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Propagation of Uncertainty from Random Error

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 Uncertainty from Systematic Error

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...

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

The Uncertainty Principle

The Uncertainty Principle

Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...

Probability Laws

Probability Laws

You might also read

Related Articles

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

Sort by

Same author

Estimating the amount of computation done by a brain using population neural activity.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Is stochastic thermodynamics the key to understanding the energy costs of computation?

Proceedings of the National Academy of Sciences of the United States of America·2024

Same author

Entropy production in communication channels.

Physical review. E·2024

Same author

Memory Systems, the Epistemic Arrow of Time, and the Second Law.

Entropy (Basel, Switzerland)·2024

Same author

Stochastic Thermodynamics of Multiple Co-Evolving Systems-Beyond Multipartite Processes.

Entropy (Basel, Switzerland)·2023

Same author

Strengthened second law for multi-dimensional systems coupled to multiple thermodynamic reservoirs.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2022

Search research articles

Related Experiment Video

Updated: Nov 28, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Uncertainty Relations and Fluctuation Theorems for Bayes Nets.

David H Wolpert¹

¹Santa Fe Institute, Santa Fe, New Mexico Complexity Science Hub, Vienna Arizona State University, Tempe, Arizona 87501, USA.

Physical Review Letters

|December 1, 2020

Summary

This study explores stochastic thermodynamics in interacting systems using Bayes nets. It introduces new fluctuation theorems for entropy production and thermodynamic uncertainty relations for these complex systems.

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

A Tactile Automated Passive-Finger Stimulator TAPS

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

Related Experiment Videos

Last Updated: Nov 28, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

A Tactile Automated Passive-Finger Stimulator TAPS

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

Area of Science:

Statistical physics
Thermodynamics
Information theory

Background:

Stochastic thermodynamics analyzes systems with inherent randomness.
Multiple interacting systems are often modeled using graphical structures like Bayes nets.
Understanding entropy production is crucial for characterizing thermodynamic processes.

Purpose of the Study:

To derive fluctuation theorems for entropy production in sets of systems within a Bayes net.
To establish conditional fluctuation theorems for entropy production.
To develop thermodynamic uncertainty relations connecting overall entropy production to individual system precisions.

Main Methods:

Bayesian network representation of multiple interacting systems.
Derivation of generalized fluctuation theorems.
Application of information-theoretic concepts to thermodynamic quantities.

Main Results:

Novel fluctuation theorems for arbitrary subsets of systems in a Bayes net.
Conditional fluctuation theorems quantifying entropy production dependencies.
Thermodynamic uncertainty relations linking system-level and network-level properties.

Conclusions:

The framework provides a unified approach to stochastic thermodynamics for complex, interacting systems.
The derived relations offer new insights into the trade-offs between thermodynamic cost and information precision.
This work advances the understanding of non-equilibrium thermodynamics in networked systems.