4 papers
Bayesian Plackett--Luce latent block models for ranked data
Lapo Santi, Nial Friel, Valeria Vitelli
We introduce a Bayesian latent block model that jointly partitions assessors and items under a Plackett--Luce observation model. Assessors are assigned to clusters and items to…
Inverse Probability Weighting in a Post-Bayesian World
Owen Thomas, William Denault, Valeria Vitelli
We present a justification of the use of Inverse Probability Weighting (IPW) in a post-Bayesian framework, in which the bias-correction provided by IPW in a frequentist context is…
Bayesian nonparametric Mallows model for clustering preference data
Lorenzo Zuccato, Veronica Vinciotti, Valeria Vitelli
Preference learning refers to the learning of latent patterns from ranking and preference data of different kinds. Typical aims of preference learning are to infer a shared consens…
Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures
Emilie Eliseussen, Haakon Muggerud, Luca Coraggio +3
With the increasing availability of ranking data, there has been a growing demand for appropriate unsupervised rank-based inferential frameworks capable of handling high-dimensiona…