Common Probability Patterns Arise from Simple Invariances

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 relat...

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Main Author: Steven A. Frank
Format: Article
Language:English
Published: MDPI AG 2016-05-01
Series:Entropy
Subjects:
Online Access:http://www.mdpi.com/1099-4300/18/5/192
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author Steven A. Frank
author_facet Steven A. Frank
author_sort Steven A. Frank
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description 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.
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spelling doaj.art-d6cff393be8c45a68ce228c3021538712022-12-22T03:59:24ZengMDPI AGEntropy1099-43002016-05-0118519210.3390/e18050192e18050192Common Probability Patterns Arise from Simple InvariancesSteven A. Frank0Department of Ecology & Evolutionary Biology, University of California, Irvine, CA 92697, USAShift 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.http://www.mdpi.com/1099-4300/18/5/192measurementmaximum entropyinformation theorystatistical mechanicsextreme value distributions
spellingShingle Steven A. Frank
Common Probability Patterns Arise from Simple Invariances
Entropy
measurement
maximum entropy
information theory
statistical mechanics
extreme value distributions
title Common Probability Patterns Arise from Simple Invariances
title_full Common Probability Patterns Arise from Simple Invariances
title_fullStr Common Probability Patterns Arise from Simple Invariances
title_full_unstemmed Common Probability Patterns Arise from Simple Invariances
title_short Common Probability Patterns Arise from Simple Invariances
title_sort common probability patterns arise from simple invariances
topic measurement
maximum entropy
information theory
statistical mechanics
extreme value distributions
url http://www.mdpi.com/1099-4300/18/5/192
work_keys_str_mv AT stevenafrank commonprobabilitypatternsarisefromsimpleinvariances