Dmytro Perepolkin
Doctoral student
Scientific methods for integrating expert knowledge in Bayesian models
Author
Summary, in English
Generating scientific advice to environmental management involves assessments with complex models, sparse data, and challenging empirical experiments, necessitating the integration of expert judgment with data into scientific models. To integrate expert judgement, assessors might elicit judgement by experts as quantiles, find a probability distribution that matches the quantiles, and add this information to the model. Data is then integrated into the model by Bayesian inference to learn parameters or make predictions. This thesis aims to simplify such
integration of expert judgment, and introduce the use of Quantile-Parameterized Distributions (QPDs) into Bayesian models. Key questions addressed include identifying suitable QPDs for encoding expert judgment, and conditions for using QPDs as priors or likelihoods in Bayesian inference. The creation of new QPDs through quantile function transformation is explored, providing a methodological advancement. The use of the proposed methodology is demonstrated on expert-informed bias-adjustment of citizen science data in a Species Distribution
Model for conservation assessment.
integration of expert judgment, and introduce the use of Quantile-Parameterized Distributions (QPDs) into Bayesian models. Key questions addressed include identifying suitable QPDs for encoding expert judgment, and conditions for using QPDs as priors or likelihoods in Bayesian inference. The creation of new QPDs through quantile function transformation is explored, providing a methodological advancement. The use of the proposed methodology is demonstrated on expert-informed bias-adjustment of citizen science data in a Species Distribution
Model for conservation assessment.
Department/s
- Centre for Environmental and Climate Science (CEC)
- BECC: Biodiversity and Ecosystem services in a Changing Climate
Publishing year
2023-12-13
Language
English
Full text
- Available as PDF - 12 MB
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Document type
Dissertation
Publisher
Lund University
Topic
- Probability Theory and Statistics
- Environmental Sciences
Keywords
- Bayesian inference
- Expert judgement
- Quantile-parameterized distributions
- Quantile functions
Status
Published
Project
- Expert Knowledge
Supervisor
- Ullrika Sahlin
- Erik Lindström
- Johan Elmberg
ISBN/ISSN/Other
- ISBN: 978-91-8039-914-2
- ISBN: 978-91-8039-915-9
Defence date
23 January 2024
Defence time
13:00
Defence place
Blå hallen, Ekologihuset.
Opponent
- John Quigley (Professor)