Bayesian Nonparametric Plackett-Luce Models for the Analysis of Preferences for College Degree Programmes

Files in This Item:
File Description SizeFormat 
insight_publication.pdf511.99 kBAdobe PDFDownload
Title: Bayesian Nonparametric Plackett-Luce Models for the Analysis of Preferences for College Degree Programmes
Authors: Caron, François
Whye Teh, Yee
Murphy, Thomas Brendan
Permanent link:
Date: 2014
Online since: 2016-12-14T11:14:28Z
Abstract: In this paper we propose a Bayesian nonparametric model for clustering partial ranking data.We start by developing a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items. Our framework is based on the theory of random atomic measures, with prior specified by a completely random measure. We characterise the posterior distribution given data, and derive a simple and effective Gibbs sampler for posterior simulation. We then develop a Dirichlet process mixture extension of our model and apply it to investigate the clustering of preferences for college degree programmes amongst Irish secondary school graduates. The existence of clusters of applicants who have similar preferences for degree programmes is established and we determine that subject matter and geographical location of the third level institution characterise these clusters.
Funding Details: European Commission
Type of material: Journal Article
Publisher: Institute of Mathematical Statistics
Journal: Annals of Applied Statistics
Volume: 8
Issue: 2
Start page: 1145
End page: 1181
Keywords: Machine learningStatisticsRanking dataPermutationsGamma processDirichlet processMixture models
DOI: 10.1214/14-AOAS717
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

Show full item record

Citations 20

Last Week
Last month
checked on Feb 12, 2019

Download(s) 50

checked on May 25, 2018

Google ScholarTM



This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.