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MENTAL MODEL · M12251

Bayes' Theorem

Bayes' Theorem
StructureHigh supportLogic
Included
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Version 1.0.0 · Updated 2026-07-30

CORE DEFINITION

Describes how to update the probability assessment of a hypothesis after obtaining new evidence. The core of the formula is: posterior probability = (likelihood × prior probability) / normalization constant.

SCAFFOLDING EFFECT

psychology

Reduce cognitive load

Dynamic correction. Do not cling to first impressions; treat every new piece of information as a 'correction factor' and continuously update your subjective probability judgments about the world.

anchor

Anchor fast decisions

Posterior ∝ prior × likelihood; new evidence adjusts beliefs according to its explanatory power for the hypothesis, forming the mathematical basis for probabilistic belief updating.

MINIMUM ACTION

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Source support: Explicit

  • link
    en.wikipedia.orghttps://en.wikipedia.org/wiki/Bayes%27_theoremZH · Explicit
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