Simplifying Bayesian inference : the general case

Détails

ID Serval
serval:BIB_3943F7503CEA
Type
Partie de livre
Collection
Publications
Titre
Simplifying Bayesian inference : the general case
Titre du livre
Model-based reasoning in scientific discovery
Auteur⸱e⸱s
Krauss S., Martignon L., Hoffrage U.
Editeur
Kluwer Academic / Plenum Publishers
Lieu d'édition
New York, NY
ISBN
978-1-4613-7181-6
978-1-4615-4813-3
Statut éditorial
Publié
Date de publication
1999
Editeur⸱rice scientifique
Magnani L., Nersessian N. J., Thagard P.
Pages
165-179
Langue
anglais
Résumé
We present empirical evidence that human reasoning follows the rules of probability theory, if information is presented in “natural formats”. Human reasoning has often been evaluated in terms of humans’ ability to deal with probabilities. Yet, in nature we do not observe probabilities, we rather count samples and their subsets. Our concept of Markov frequencies generalizes Gigerenzer and Hoffrage’s “natural frequencies”, which are known to foster insight in Bayesian situations with one cue. Markov frequencies allow to visualize Bayesian inference problems even with an arbitrary number of cues.
Création de la notice
24/02/2009 15:34
Dernière modification de la notice
20/08/2019 14:28
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