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Bayesian nonparametric Plackett-Luce models for the analysis of clustered ranked data

Francois Caron 1, 2 Yee Whye Teh 3 Thomas Brendan Murphy 4 
1 ALEA - Advanced Learning Evolutionary Algorithms
Inria Bordeaux - Sud-Ouest, UB - Université de Bordeaux, CNRS - Centre National de la Recherche Scientifique : UMR5251
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 clustering the preferences for university programmes of Irish secondary school graduates.
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https://hal.inria.fr/hal-00755478
Contributor : Francois Caron Connect in order to contact the contributor
Submitted on : Wednesday, November 21, 2012 - 1:34:00 PM
Last modification on : Friday, December 3, 2021 - 12:20:04 PM
Long-term archiving on: : Saturday, December 17, 2016 - 1:00:43 PM

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  • HAL Id : hal-00755478, version 1
  • ARXIV : 1211.5037

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Francois Caron, Yee Whye Teh, Thomas Brendan Murphy. Bayesian nonparametric Plackett-Luce models for the analysis of clustered ranked data. [Research Report] RR-8143, 2012. ⟨hal-00755478v1⟩

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