From Distinguishability to Information

This paper explains how observations change the probabilities assigned to possible states and how information can be understood as a reduction in uncertainty. A simple example introduces Bayesian updating and distinguishes the assessment of evidence from the choice of an action. Further examples introduce the bit and Shannon entropy, explain why repeated observations need not provide independent evidence, and clarify why the amount of information does not by itself establish truth or meaning.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-08
DOI
https://doi.org/10.5281/zenodo.24060044
Primary Topic
Bayesian Modeling and Causal Inference
Type
article
Field-Weighted Citation Impact
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article

From Distinguishability to Information

Michael Shanidze
Zenodo (CERN European Organization for Nuclear Research)
Bayesian Modeling and Causal Inference
article

From Distinguishability to Information

Michael Shanidze
article en

Abstract

This paper explains how observations change the probabilities assigned to possible states and how information can be understood as a reduction in uncertainty. A simple example introduces Bayesian updating and distinguishes the assessment of evidence from the choice of an action. Further examples introduce the bit and Shannon entropy, explain why repeated observations need not provide independent evidence, and clarify why the amount of information does not by itself establish truth or meaning.

Zenodo (CERN European Organization for Nuclear Research)
Bayesian Modeling and Causal Inference
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From Distinguishability to Information — Michael Shanidze · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS