PreliZ: A tool-box for prior elicitation

Abstract

In a Bayesian modeling workflow, a prior distribution can be chosen in different ways as long as it captures the uncertainty about model parameters prior to observing any data. Particularly, prior elicitation refers to the process of transforming the knowledge of a particular domain into well-defined probability distributions. Here we introduce PreliZ, a Python package aimed at helping practitioners choose prior distributions.