Informative Priors for the Consensus Ranking in the Bayesian Mallows Model

IF 4.9 2区数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Bayesian Analysis Pub Date : 2022-01-01 DOI:10.1214/22-ba1307

Marta Crispino, Isadora Antoniano-Villalobos

引用次数: 1

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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贝叶斯Mallows模型中一致性排序的信息先验

这项工作的目的是研究Mallows模型中具有Spearman距离的一致性排序的先验启发问题，Spearman是一种流行的基于距离的排序或排列数据模型。用于这种模型的先前贝叶斯推断已被限制为在排列空间上使用一致先验。我们提出了一种新的策略来引出关于模型位置参数的信息先验信念，讨论了超参数的解释以及先验选择对后验分析的意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Bayesian Analysis 数学-数学跨学科应用

CiteScore

6.50

自引率

13.60%

发文量

审稿时长

>12 weeks

期刊介绍： Bayesian Analysis is an electronic journal of the International Society for Bayesian Analysis. It seeks to publish a wide range of articles that demonstrate or discuss Bayesian methods in some theoretical or applied context. The journal welcomes submissions involving presentation of new computational and statistical methods; critical reviews and discussions of existing approaches; historical perspectives; description of important scientific or policy application areas; case studies; and methods for experimental design, data collection, data sharing, or data mining. Evaluation of submissions is based on importance of content and effectiveness of communication. Discussion papers are typically chosen by the Editor in Chief, or suggested by an Editor, among the regular submissions. In addition, the Journal encourages individual authors to submit manuscripts for consideration as discussion papers.