Informative Priors for the Consensus Ranking in the Bayesian Mallows Model

IF 5.4 3区材料科学 Q2 CHEMISTRY, PHYSICAL

ACS Applied Energy Materials 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

ACS Applied Energy Materials Materials Science-Materials Chemistry

CiteScore

10.30

自引率

6.20%

发文量

1368

期刊介绍： ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.