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Bayesian nonparametric models for ranked data

Abstract:
We develop 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 the prior specified by a gamma process. We derive a posterior characterization and a simple and effective Gibbs sampler for posterior simulation. We develop a time-varying extension of our model, and apply it to the New York Times lists of weekly bestselling books.

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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Journal:
Advances in Neural Information Processing Systems
Volume:
2
Pages:
1520-1528
Publication date:
2012-11-19
ISSN:
1049-5258
URN:
uuid:1b517333-e0a0-4c5b-89ef-df31998738b1
Source identifiers:
364039
Local pid:
pubs:364039
Language:
English
Keywords:

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