Metropolis-Hastings algorithm

Modelling unobserved heterogeneity of ranking data with the Bayesian mixture of Extended Plackett-Luce models

The Plackett-Luce distribution (PL) is one of the most successful parametric options within the class of multistage ranking models to learn the preferences on a given set of items from a sample of ordered sequences. It postulates that the ranking process is carried out by sequentially assigning the positions according to the forward order, that is, from the top (most-liked) to the bottom (least-liked) alternative. This assumption has been relaxed with the Extended Plackett-Luce model (EPL), thanks to the introduction of the reference order parameter describing the rank attribution path.

© Università degli Studi di Roma "La Sapienza" - Piazzale Aldo Moro 5, 00185 Roma