« Principe d'indifférence » : différence entre les versions


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[https://deepai.org/machine-learning-glossary-and-terms/principle-of-indifference  Source : DeepAI.org ]
[https://deepai.org/machine-learning-glossary-and-terms/principle-of-indifference  Source : DeepAI.org ]
[[Catégorie:DeepAI.org]]


[[Catégorie:vocabulary]]
[[Catégorie:vocabulary]]

Version du 15 décembre 2020 à 19:10

en construction

Définition

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Français

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Anglais

Principle of Indifference

The principle of indifference is a rule for assigning possible outcomes equal probability when no other information is available. If there’s no data suggesting one possibility is more likely than another, then potential outcomes are reduced to the fewest logical, mutually exclusive choices possible, with the remaining general outcomes simply divided by one (1/n) to determine the probability of occurrence.

This principle of indifference isn’t used in Frequentist probability, since it expresses a degree of belief rather than frequency. However, in Bayesian probability, this approach is sometimes employed as a non-informative prior (objective reasoning) when there’s no other available data and disagreement over what subjective prior assumptions to make.



Source : DeepAI.org



Contributeurs: Claire Gorjux, wiki