Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, vol. 2 (2007) pp. 556-563
The Indian buffet process (IBP) is a Bayesian nonparametric distribution whereby objects are modelled using an unbounded number of latent features. In this paper we derive a stick-breaking representation for the IBP. Based on this new rep- resentation, we develop slice samplers for the IBP that are efficient, easy to implement and are more generally applicable than the currently available Gibbs sampler. This representation, along with the work of Thibaux and Jordan (17), also illuminates interesting theoretical connec- tions between the IBP, Chinese restaurant pro- cesses, Beta processes and Dirichlet processes.
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