Bayesian orthodoxy posits a tight relationship between con-ditional probability and updating. Namely, the probability of an event A after learning an event B should equal the condi-tional probability of A given B prior to learning B. We ex-amine whether ordinary judgment conforms to the orthodox view. In three experiments we found substantial differences between the conditional probability of an event A supposing an event B compared to the probability of A after having learned B. Specifically, supposing B appears to have less impact on the credibility of A than learning that B is true. Thus, Bayesian up-dating seems not to describe the relation between the probabil-ity distribution that arises from learning an event B compared to merely sup...
This experiment studies belief updating under ambiguity, using subjects' bid and ask prices for an a...
Extensive research in the behavioral sciences has addressed people’s ability to learn stationary pro...
This paper characterizes several belief-revision rules in a uni\u85ed framework: Bayesian revision u...
Some of the information we receive comes to us in an explicitly conditional form. It is an open ques...
International audienceIn our laboratory experiment, subjects, in sequence, have to predict the value...
In our laboratory experiment, subjects, in sequence, have to predict the value of a good. The secon...
In this article, we address a major outstanding question of probabilistic Bayesian epistemology: `Ho...
Bayesian inference is limited in scope because it cannot be applied in idealized contexts where none...
The Bayesian model has been used in psychology as the standard reference for the study of probabilit...
In a probability-based reasoning system, Bayes' theorem and its variations are often used to re...
The talk will trace various connections between update, probability and belief. We look at various w...
We present a conservative extension of a Bayesian account of confirmation that can deal with the pro...
This paper explains the empirical phenomenon of persistent \u85 fty-\u85ftyprob-ability judgments th...
The rational status of the Bayesian calculus for revising likelihoods is compromised by the common b...
We present a conservative extension of a Bayesian account of confirmation that can deal with the pro...
This experiment studies belief updating under ambiguity, using subjects' bid and ask prices for an a...
Extensive research in the behavioral sciences has addressed people’s ability to learn stationary pro...
This paper characterizes several belief-revision rules in a uni\u85ed framework: Bayesian revision u...
Some of the information we receive comes to us in an explicitly conditional form. It is an open ques...
International audienceIn our laboratory experiment, subjects, in sequence, have to predict the value...
In our laboratory experiment, subjects, in sequence, have to predict the value of a good. The secon...
In this article, we address a major outstanding question of probabilistic Bayesian epistemology: `Ho...
Bayesian inference is limited in scope because it cannot be applied in idealized contexts where none...
The Bayesian model has been used in psychology as the standard reference for the study of probabilit...
In a probability-based reasoning system, Bayes' theorem and its variations are often used to re...
The talk will trace various connections between update, probability and belief. We look at various w...
We present a conservative extension of a Bayesian account of confirmation that can deal with the pro...
This paper explains the empirical phenomenon of persistent \u85 fty-\u85ftyprob-ability judgments th...
The rational status of the Bayesian calculus for revising likelihoods is compromised by the common b...
We present a conservative extension of a Bayesian account of confirmation that can deal with the pro...
This experiment studies belief updating under ambiguity, using subjects' bid and ask prices for an a...
Extensive research in the behavioral sciences has addressed people’s ability to learn stationary pro...
This paper characterizes several belief-revision rules in a uni\u85ed framework: Bayesian revision u...