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Hybrid elicitation and quantile-parametrized likelihood

Perepolkin, Dmytro LU orcid ; Goodrich, Benjamin and Sahlin, Ullrika LU (2024) In Statistics and Computing 34.
Abstract

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... (More)

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.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Bayesian analysis, Expert knowledge elicitation, Indirect inference, Quantile-based distributions, Quantile-parameterized distributions
in
Statistics and Computing
volume
34
article number
11
publisher
Springer
external identifiers
  • scopus:85175016803
ISSN
0960-3174
DOI
10.1007/s11222-023-10325-0
language
English
LU publication?
yes
id
1b674657-c281-4e4c-8709-f08b3f368cf2
date added to LUP
2023-12-11 13:26:19
date last changed
2024-04-10 13:58:01
@article{1b674657-c281-4e4c-8709-f08b3f368cf2,
  abstract     = {{<p>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.</p>}},
  author       = {{Perepolkin, Dmytro and Goodrich, Benjamin and Sahlin, Ullrika}},
  issn         = {{0960-3174}},
  keywords     = {{Bayesian analysis; Expert knowledge elicitation; Indirect inference; Quantile-based distributions; Quantile-parameterized distributions}},
  language     = {{eng}},
  publisher    = {{Springer}},
  series       = {{Statistics and Computing}},
  title        = {{Hybrid elicitation and quantile-parametrized likelihood}},
  url          = {{http://dx.doi.org/10.1007/s11222-023-10325-0}},
  doi          = {{10.1007/s11222-023-10325-0}},
  volume       = {{34}},
  year         = {{2024}},
}