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.

Informative Priors for the Consensus Ranking in the Bayesian Mallows Model

Antoniano-Villalobos, Isadora
2022

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/5004033
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