Hybrid elicitation and quantile-parametrized likelihood

Summary, in English

This paper extends the application of quantile-based Bayesian inference to probability distributions defined in terms of quantiles of observable quantities. Quantile-parameterized 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.

• Author: Dmytro Perepolkin; Benjamin Goodrich; Ullrika Sahlin
• Department/s: Centre for Environmental and Climate Science (CEC); BECC: Biodiversity and Ecosystem services in a Changing Climate; Computational Science for Health and Environment; MERGE: ModElling the Regional and Global Earth system
• Publishing year: 2024-02
• Language: English
• Publication/Series: Statistics and Computing
• Volume: 34
• Links: http://dx.doi.org/10.1007/s11222-023-10325-0
• Document type: Journal article
• Publisher: Springer
• Topic: Probability Theory and Statistics
• Keywords: Bayesian analysis; Expert knowledge elicitation; Indirect inference; Quantile-based distributions; Quantile-parameterized distributions
• Status: Published
• Research group: Computational Science for Health and Environment
• ISBN/ISSN/Other: ISSN: 0960-3174