Informative extended Mallows priors in the Bayesian Mallows model
Methodology
2019-01-31 v1
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.
Keywords
Cite
@article{arxiv.1901.10870,
title = {Informative extended Mallows priors in the Bayesian Mallows model},
author = {Marta Crispino and Isadora Antoniano-Villalobos},
journal= {arXiv preprint arXiv:1901.10870},
year = {2019}
}