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Hybrid elicitation and indirect Bayesian inference with quantile-parametrized likelihood

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  • Perepolkin, Dmytro
  • Goodrich, Benjamin
  • Sahlin, Ullrika

Abstract

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.

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  • Perepolkin, Dmytro & Goodrich, Benjamin & Sahlin, Ullrika, 2021. "Hybrid elicitation and indirect Bayesian inference with quantile-parametrized likelihood," OSF Preprints paby6, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:paby6
    DOI: 10.31219/osf.io/paby6
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    Cited by:

    1. Baey, Charlotte & Smith, Henrik G. & Rundlöf, Maj & Olsson, Ola & Clough, Yann & Sahlin, Ullrika, 2023. "Calibration of a bumble bee foraging model using Approximate Bayesian Computation," Ecological Modelling, Elsevier, vol. 477(C).
    2. Perepolkin, Dmytro & Lindsröm, Erik & Sahlin, Ullrika, 2023. "Quantile-parameterized distributions for expert knowledge elicitation," OSF Preprints tq3an, Center for Open Science.

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