In this paper, we investigate the use of assessments of conditional previsions for modeling prior information on the parameter of a binomial model as a way of obtaining non-vacuous posterior previsions via natural extension. More specifically, we argue that a useful method for obtaining an imprecise prevision for the parameter q of a binomial model, given a sample of size n showing r successes, is to assess imprecise previsions for q which are conditional on samples having sizes larger than n. Inferences obtained using this approach are compared to Walley's proposal for learning from a bag of marbles.
Keywords. conditional lower previsions, prior information modeling, natural extension, generalized Bayes rule, binomial model
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Authors addresses:
1800, boul. Lionel-Boulet
Varennes (Québec)
CANADA J3X 1S1
E-mail addresses:
| Vincent Fortin | vinfort@ireq.ca |