Central limit theorems for an Indian buffet model with random weights

The three-parameter Indian buffet process is generalized. The possibly different role played by customers is taken into account by suitable (random) weights. Various limit theorems are also proved for such generalized Indian buffet process. Let L_n be the number of dishes experimented by the first n customers, and let {\overline K}_n = 1/n \sum_{i=1}^n K_i where K_i is the number of dishes tried by customer i. The asymptotic distributions of L_n and {\overline K}_n, suitably centered and scaled, are obtained. The convergence turns out to be stable (and not only in distribution). As a particular case, the results apply to the standard (i.e., non generalized) Indian buffet process.