We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
The Prior Can Often Only Be Understood in the Context of the Likelihood.
- Authors
Gelman, Andrew; Simpson, Daniel; Betancourt, Michael
- Abstract
A key sticking point of Bayesian analysis is the choice of prior distribution, and there is a vast literature on potential defaults including uniform priors, Jeffreys' priors, reference priors, maximum entropy priors, and weakly informative priors. These methods, however, often manifest a key conceptual tension in prior modeling: a model encoding true prior information should be chosen without reference to the model of the measurement process, but almost all common prior modeling techniques are implicitly motivated by a reference likelihood. In this paper we resolve this apparent paradox by placing the choice of prior into the context of the entire Bayesian analysis, from inference to prediction to model evaluation.
- Subjects
BAYESIAN analysis; ENTROPY; ENCODING; DEFAULT reasoning; INFERENCE (Logic)
- Publication
Entropy, 2017, Vol 19, Issue 10, p555
- ISSN
1099-4300
- Publication type
Article
- DOI
10.3390/e19100555