Informative extended Mallows priors in the Bayesian Mallows model

Marta Crispino; Isadora Antoniano-Villalobos

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arXiv · 1901.10870

Informative extended Mallows priors in the Bayesian Mallows model

Marta Crispino·Isadora Antoniano-Villalobos

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Abstract

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.

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Marta Crispino, Isadora Antoniano-Villalobos. 2019-01-30. Informative extended Mallows priors in the Bayesian Mallows model. https://arxiv.org/abs/1901.10870

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