  The aim of this work is to study the problem of prior elicitation for the
Mallows model with Spearman's distance, a popular distance-based model for
rankings or permutation data. Previous Bayesian inference for such model has
been limited to the use of the uniform prior over the space of permutations. We
present a novel strategy to elicit subjective prior beliefs on the location
parameter of the model, discussing the interpretation of hyper-parameters and
the implication of prior choices for the posterior analysis.
