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Bayesian inference for rank-order problems is frustrated by the absence of anexplicit likelihood function. This hurdle can be overcome by assuming a latentnormal representation that is consistent with the ordinal information in thedata: the observed ranks are conceptualized as an impoverished reflection of anunderlying continuous scale, and inference concerns the parameters that governthe latent representation. We apply this generic data-augmentation method toobtain Bayes factors for three popular rank-based tests: the rank sum test, thesigned rank test, and Spearman's $\rho_s$.

Bayesian Rank-Based Hypothesis Testing for the Rank Sum Test, the Signed Rank Test, and Spearman's $\rho$