A choice model with infinitely many latent features

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Sharing Features among Dynamical Systems with Beta Processes (2009, methodology): “One early approach to approximate Gibbs sampling in non-con jugate IBP models relies on a finite truncation [7].”
Bayesian Nonparametrics: Hierarchical Bayesian nonparametric models with applications (2010, methodology): “The IBP was originally defined directly as a distribution on (equivalence classes of) binary matrices by Griffiths and Ghahramani (2006) and Ghahramani et al.”
Stick-breaking Construction for the Indian Buffet Process (2007, methodology): “It i s possible to devise samplers in the non-conjugate case analogous to those developed for DP mixture models [10, 5].”
Matrix estimation by Universal Singular Value Thresholding (2012, background): “More recently, several authors have proposed to perform Bayesian inference for (generalized) Bradley–Terry models [2, 48, 49, 50, 52, 28].”
Nonparametric Latent Feature Models for Link Prediction (2009, methodology): “We use the approach from [18] for sampling of α.”
The Indian Buffet Process: An Introduction and Review (2011, methodology): “Görür et al. (2006) present a nonparametric Bayesian model based on the IBP which, given the choice data, infers latent features of the options and the corresponding weights of these features.”