Probabilistic Foundations of Statistical Mechanics: A Bayesian Approach
B. Buck, A.C. Merchant

TL;DR
This paper develops a Bayesian framework for statistical mechanics, linking entropy to uncertainty, and explores the limitations of isentropic processes in quantum systems, challenging traditional thermodynamic assumptions.
Contribution
It introduces a Bayesian approach to statistical mechanics, clarifies the nature of entropy, and reveals fundamental constraints on isentropic processes in quantum systems.
Findings
Entropy depends explicitly on data and Hamiltonian.
Fluctuations are negligible in macroscopic systems.
Quantum processes cannot generally be both isentropic and reversible.
Abstract
We examine the fundamental aspects of statistical mechanics, dividing the problem into a discussion purely about probability, which we analyse from a Bayesian standpoint. We argue that the existence of a unique maximising probability distribution for states labelled by given data implies that the corresponding maximal value of the information entropy depends explicitly on the data at equilibrium and on the Hamiltonian of the system. As such, it is a direct measure of our uncertainty about the exact state of the body and can be identified with the traditional thermodynamic entropy. We consider the well known microcanonical, canonical and grand canonical methods and ensure that the fluctuations about mean values are generally minuscule for macroscopic systems before identifying these mean values…
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Taxonomy
TopicsProbabilistic and Robust Engineering Design
