Logo eprints

Central Limit Theorems for an Indian Buffet Model with Random Weights

Berti, Patrizia and Crimaldi, Irene and Pratelli, Luca and Rigo, Pietro Central Limit Theorems for an Indian Buffet Model with Random Weights. Technical Report # /2013 (Submitted)

WarningThere is a more recent version of this item available.
Full text not available from this repository.


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.

Item Type: Working Paper (Technical Report)
Additional Information: Preprint, Submitted
Uncontrolled Keywords: Bayesian nonparametrics, Central limit theorem, Conditional identity in distribution, Indian buffet process, Random measure, Random reinforcement, Stable convergence
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
Research Area: Economics and Institutional Change
Depositing User: Irene Crimaldi
Date Deposited: 16 Apr 2013 14:59
Last Modified: 24 Jan 2014 14:08
URI: http://eprints.imtlucca.it/id/eprint/1544

Available Versions of this Item

Actions (login required)

Edit Item Edit Item