TY  - RPRT
UR  - http://arxiv.org/pdf/1304.3626v1.pdf
AV  - none
TI  - Central Limit Theorems for an Indian Buffet Model with Random Weights
Y1  - 2013/04/12/
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
M1  - technical_report
N2  - The three-parameter Indian buffet process is generalized. T
he 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.
A1  - Berti, Patrizia
A1  - Crimaldi, Irene
A1  - Pratelli, Luca
A1  - Rigo, Pietro
EP  - 17
N1  - Preprint, Submitted
ID  - eprints1544
ER  -