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Vor einem Tag · Entropy in information theory is directly analogous to the entropy in statistical thermodynamics. The analogy results when the values of the random variable designate energies of microstates, so Gibbs's formula for the entropy is formally identical to Shannon's formula.
Vor 4 Tagen · The statistical definition of entropy defines it in terms of the statistics of the motions of the microscopic constituents of a system – modeled at first classically, e.g. Newtonian particles constituting a gas, and later quantum-mechanically (photons, phonons, spins, etc.). The two approaches form a consistent, unified view of the ...
1. Mai 2024 · Entropy is central to statistical physics, but it has multiple meanings. This Review clarifies the strengths of each use and the connections between them, seeking to bolster crosstalk between...
Vor 5 Tagen · Entropy. Differential entropy. Conditional entropy. Joint entropy. Mutual information. Directed information. Conditional mutual information. Relative entropy. Entropy rate. Limiting density of discrete points. Asymptotic equipartition property. Rate–distortion theory. Shannon's source coding theorem. Channel capacity. Noisy-channel coding theorem.
Vor 6 Tagen · Entropy stands as a foundational concept in information theory, initially formulated by Claude Shannon ( 1948 ). Its applications extend across diverse fields such as thermodynamics, communication theory, computer science, biology, economics, and statistics (Cover and Thomas 1991 ).
9. Mai 2024 · In one statistical interpretation of entropy, it is found that for a very large system in thermodynamic equilibrium, entropy S is proportional to the natural logarithm of a quantity Ω representing the maximum number of microscopic ways in which the macroscopic state corresponding to S can be realized; that is, S = k ln Ω, in which ...
1. Mai 2024 · Entropy, irreversibility and inference at the foundations of statistical physics | Semantic Scholar. DOI: 10.1038/s42254-024-00720-5. Corpus ID: 269514691. Entropy, irreversibility and inference at the foundations of statistical physics. Jonathan Asher Pachter, Ying-Jen Yang, Ken A. Dill. Published in Nature Reviews Physics 1 May 2024. Physics.
