TY  - JOUR
JF  - The Annals of Applied Probability
Y1  - 2015///
AV  - none
ID  - eprints2612
SP  - 523
KW  - Bayesian nonparametrics
KW  -  Central limit theorem
KW  -  Conditional identity in distribution
KW  -  Indian buffet process
KW  -  Random measure
KW  -  Random reinforcement
KW  -  Stable convergence
UR  - http://projecteuclid.org/euclid.aoap/1424355122
IS  - 2
SN  - 1050-5164
VL  - 25
PB  - Institute of Mathematical Statistics
A1  - Berti, Patrizia
A1  - Crimaldi, Irene
A1  - Pratelli, Luca
A1  - Rigo, Pietro
EP  - 547
N2  - 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.
TI  - Central Limit Theorems for an Indian Buffet Model with Random Weights
ER  -