The Wasserstein Impact Measure (WIM): A practical tool for quantifying prior impact in Bayesian statistics

The prior distribution is a crucial building block in Bayesian analysis, and its choice will impact the subsequent inference. It therefore important to have convenient way quantify this impact, as such measure of help choose between two or more priors given situation. To end new approach, Wasserstein Impact Measure (WIM), introduced. In three simulated scenarios, WIM compared competitor measures from literature, versatility illustrated via real datasets.