Work

Bayesian nonparametric models for ranked data

Year: 2012

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1211.4321

Authors: Caron, Francois, Teh, Yee Whye

Institutions: Institut national de recherche en sciences et technologies du numérique, Centre Inria de l'université de Bordeaux, Institut de Mathématiques de Bordeaux, University of Oxford

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2950076180

Cites:

Cited by: 21

Related to: 10