elicito: A Python Package for Expert Prior Elicitation

TL;DR

elicito is a Python package designed to facilitate expert prior elicitation in Bayesian analysis through a modular, simulation-based framework supporting various elicitation methods and customizable components.

Contribution

The paper introduces elicito, a flexible Python tool that streamlines expert prior elicitation with a modular design supporting diverse methods and customization options.

Findings

Demonstrated through a case study, showcasing practical application.

Provides transparency and reproducibility in the elicitation process.

Supports both structural and predictive elicitation methods.

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.