This paper extends the application of quantilebased Bayesian inference to probability distributions defined in terms of quantiles of observable quantities. Quantileparameterized distributions are characterized by high shape flexibility and parameter interpretability, making them useful for eliciting information about observables. To encode uncertainty in the quantiles elicited from experts, we propose a Bayesian model based on the metalog distribution and a variant of the Dirichlet prior. We discuss the resulting hybrid expert elicitation protocol, which aims to characterize uncertainty in parameters by asking questions about observable quantities. We also compare and contrast this approach with parametric and predictive elicitation methods.
 Award ID(s):
 1740858
 NSFPAR ID:
 10294762
 Date Published:
 Journal Name:
 Entropy
 Volume:
 23
 Issue:
 6
 ISSN:
 10994300
 Page Range / eLocation ID:
 740
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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