# Informative extended Mallows priors in the Bayesian Mallows model

## TL;DR

This paper introduces a new method for eliciting informative priors in the Bayesian Mallows model with Spearman's distance, moving beyond the traditional uniform prior to incorporate subjective beliefs, thereby enhancing inference quality.

## Contribution

It proposes a novel strategy for prior elicitation in the Bayesian Mallows model, clarifying hyper-parameter interpretation and impact on posterior analysis.

## Key findings

- New prior elicitation method developed
- Hyper-parameter interpretation clarified
- Implications for posterior inference discussed

## 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.