Hybrid elicitation and quantile-parametrized likelihood
(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.
(Less)
- author
- Perepolkin, Dmytro
LU
; Goodrich, Benjamin and Sahlin, Ullrika LU
- organization
- publishing date
- 2024-02
- 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
- 2024-06-19 11:58:24
@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}}, }