Informative Priors for the Consensus Ranking in the Bayesian Mallows Model
Marta Crispino, Isadora Antoniano‐Villalobos · Bayesian Analysis · 2022
The aim of this work is to study the problem of prior elicitation for the consensus ranking in the Mallows model with Spearman’s distance, a popular distance-based model for rankings or permutation data. Previous Bayesian inference for such a model has been limited to the use of the uniform prior over the space of permutations. We present a novel strategy to elicit informative 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.