In Bayesian analysis, prior elicitation, or the process of facilitating the expression of one’s beliefs to inform statistical modeling, is an essential yet challenging step. Analysts often have beliefs about real-world variables and their relationships. However, existing tools require analysts to translate these beliefs and express them indirectly as probability distributions over model parameters. We present PriorWeaver, an interactive visualization system that facilitates prior elicitation through iterative dataset construction and refinement. Analysts visually express their assumptions about individual variables and their relationships. Under the hood, these assumptions create a dataset used to derive statistical priors. Prior predictive checks then help analysts compare the priors to their assumptions. In a lab study with 17 participants new to Bayesian analysis, we compare PriorWeaver to a baseline incorporating existing techniques. Compared to the baseline, PriorWeaver gave participants greater control, clarity, and confidence, leading to priors that were better aligned with their expectations.
ACM CHI Conference on Human Factors in Computing Systems