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Bayesian estimation of Thurstonian ranking models based on the Gibbs sampler
Author(s) -
Yao Grace,
Böckenholt Ulf
Publication year - 1999
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1348/000711099158973
Subject(s) - gibbs sampling , ranking (information retrieval) , bayesian probability , econometrics , estimation , statistics , mathematics , computer science , artificial intelligence , economics , management
This paper presents a Gibbs sampler for the estimation of Thurstonian ranking models. This approach is useful for the analysis of ranking data with a large number of options. Approaches for assessing the goodness‐of‐fit of Thurstonian ranking models based on posterior predictive distributions are also discussed. Two simulation studies and two ranking studies are presented to illustrate that the Gibbs sampler is a promising solution to the numerical problems that previously plagued the estimation of Thurstonian ranking models.