Common probability patterns arise from simple invariances

TL;DR

This paper demonstrates that simple invariances like shift, stretch, and rotation are sufficient to generate the common probability distributions observed in nature, without needing assumptions from statistical mechanics or information theory.

Contribution

It emphasizes the primacy of invariance principles in deriving probability patterns, providing a unified explanation for various distributions.

Findings

Invariance principles generate exponential and Gaussian distributions.

Scaling relations from invariances produce observed probability patterns.

Classical methods require additional assumptions, unlike invariance-based explanations.

Abstract

Shift and stretch invariance lead to the exponential-Boltzmann probability distribution. Rotational invariance generates the Gaussian distribution. Particular scaling relations transform the canonical exponential and Gaussian patterns into the variety of commonly observed patterns. The scaling relations themselves arise from the fundamental invariances of shift, stretch, and rotation, plus a few additional invariances. Prior work described the three fundamental invariances as a consequence of the equilibrium canonical ensemble of statistical mechanics or the Jaynesian maximization of information entropy. By contrast, I emphasize the primacy and sufficiency of invariance alone to explain the commonly observed patterns. Primary invariance naturally creates the array of commonly observed scaling relations and associated probability patterns, whereas the classical approaches derived from statistical mechanics or information theory require special assumptions to derive commonly observed scales.