Hybrid elicitation and indirect Bayesian inference with quantile-parametrized likelihood

Dmytro Perepolkin, Benjamin Goodrich, Ullrika Sahlin

Forskningsoutput: Övriga bidragÖvrigtForskning

Sammanfattning

This paper extends the application of indirect Bayesian inference to probability distributions defined in terms of quantiles of the observable quantities. Quantile-parameterized distributions are characterized by high shape flexibility and interpretability of its parameters, and are therefore useful for elicitation on
observables. To encode uncertainty in the quantiles elicited from experts, we propose a Bayesian model based on the metalog distribution and a version of the Dirichlet prior. The resulting “hybrid” expert elicitation protocol for characterizing uncertainty in parameters using questions about the observable quantities is discussed and contrasted to parametric and predictive elicitation.
Originalspråkengelska
FörlagOSF
Antal sidor34
Utgivningsorthttps://doi.org/10.31219/osf.io/paby6
DOI
StatusE-pub ahead of print - 2021 sep. 27

Ämnesklassifikation (UKÄ)

  • Sannolikhetsteori och statistik

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