Researchers have developed an AI tool called Distribird that helps build informative prior distributions for Bayesian model calibration. This process is typically slow and requires both domain and statistical expertise. Distribird automates the process by searching scientific literature, extracting relevant values, and fitting a probability distribution. The tool evaluates the quality of its priors against a single-prompt language model baseline and outperforms it in certain aspects. Distribird's properties make it suitable for scientific use, particularly in cases where domain knowledge exists in published literature. --- Why it matters: This matters to researchers working with Bayesian models because it provides an efficient way to build informative prior distributions, which is a crucial step in calibration. By automating this process, Distribird can save time and effort, allowing researchers to focus on other aspects of their work. Source: https://arxiv.org/abs/2608.11210