%D 2015
%L eprints2612
%X 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 
{\bar 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 {\bar 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.
%A Patrizia Berti
%A Irene Crimaldi
%A Luca Pratelli
%A Pietro Rigo
%K Bayesian nonparametrics, Central limit theorem, Conditional identity in distribution, Indian buffet process, Random measure, Random reinforcement, Stable convergence
%N 2
%J The Annals of Applied Probability
%R DOI: 10.1214/14-AAP1002
%T Central Limit Theorems for an Indian Buffet Model with Random Weights
%P 523-547
%I Institute of Mathematical Statistics
%V 25