Scientific methods for integrating expert knowledge in Bayesian models

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.