Information Theory and Statistical Mechanics.

Jaynes, E. T. (1957). Information Theory and Statistical Mechanics. Physical Review, 106(4), 620-630.

Type

Journal article

Intellectual base

Primary research

Year

1957

DOI

10.1103/physrev.106.620

Link

https://doi.org/10.1103/PhysRev.106.620

Cited from

physics

Cited by

5 citations across 4 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Degrees of Freedom
  • In information and machine learning contexts, Jaynes' maximum-entropy principle

    This sourceDerives the canonical (and other) ensembles as maximum-entropy distributions subject to constraints on known observables; supplies the information-theoretic principle that the fewest DOFs consistent with the constraints minimize model complexity.

• Ensemble
  • The information-theoretic foundation for ensemble derivation comes from Jaynes

    This sourcederives canonical and other ensembles as maximum-entropy distributions subject to constraints on known observables, grounding ensembles as a consequence of inference under partial information.

• Entropy (Thermodynamic Sense)
  • ) and Jaynes (1957

    This sourceDerives the canonical (and other) ensembles as maximum-entropy distributions subject to constraints; establishes the information-theoretic foundation linking Shannon and Gibbs/Boltzmann entropy.

Domain-specific

• Maximum entropy thermodynamics
  • MaxEnt thermodynamics treats equilibrium ensembles as least-committal inferences from incomplete macroscopic information.
  • The entry records a descriptive analytical identity; practical use requires the governing domain's evidence, standards, and safety obligations.

Verification

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