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MENTAL MODEL · M6218

Principle of Maximum Entropy

Principle of Maximum Entropy
TechnicalHigh supportStatistical Mechanics
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Updated 2026-08-05

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INTRODUCTION

English translation pending.

CORE DEFINITION

Formulated by E. T. Jaynes as a principle of inference, maximum entropy states that when choosing a probability distribution consistent with all known constraints, one should select the distribution with the largest entropy, since it assumes the least beyond what is known. It generalizes the principle of insufficient reason to continuous distributions. The result is a baseline that is maximally noncommittal, and it is used in statistical mechanics, spectral estimation and image reconstruction.

SCAFFOLDING EFFECT

psychology

Reduce cognitive load

- State constraints only: include what you actually know and leave everything else unconstrained. - Choose the flattest fit: prefer the distribution that adds no assumptions beyond the constraints. - Treat it as a baseline: use maximum entropy as a neutral default rather than a claim about reality.

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Every additional assumption about a distribution is information that was not supplied by the data, so assuming more than the constraints justify can only add error. Maximizing entropy selects the distribution that encodes exactly the known constraints and nothing else. This makes the resulting estimate the least committal one available, which is why it is used as a neutral prior.

MINIMUM ACTION

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