🤖 AI Summary
Expert priors are typically elicited from domain experts based on observable quantities rather than model parameters, making direct translation into Bayesian prior distributions challenging.
Method: We propose a modular, simulation-based framework for expert prior elicitation that systematically maps expert judgments about observables to computationally tractable parameter priors. Our approach features a configurable architecture that decouples the generative model, expert input format, prior assumptions, and loss function—supporting both structured and predictive elicitation paradigms. It integrates simulation-based inference, parametric and nonparametric prior modeling, and interactive interfaces to enhance transparency, reproducibility, and methodological comparability.
Contribution/Results: We release *elicito*, an open-source Python package with a comprehensive API and empirical case studies, demonstrating its efficiency and robustness in bridging the gap between expert knowledge and probabilistic modeling in real-world applications.
📝 Abstract
Expert prior elicitation plays a critical role in Bayesian analysis by enabling the specification of prior distributions that reflect domain knowledge. However, expert knowledge often refers to observable quantities rather than directly to model parameters, posing a challenge for translating this information into usable priors. We present elicito, a Python package that implements a modular, simulation-based framework for expert prior elicitation. The framework supports both structural and predictive elicitation methods and allows for flexible customization of key components, including the generative model, the form of expert input, prior assumptions (parametric or nonparametric), and loss functions. By structuring the elicitation process into configurable modules, elicito offers transparency, reproducibility, and comparability across elicitation methods. We describe the methodological foundations of the package, its software architecture, and demonstrate its functionality through a case study.