@techreport{eprints1544,
            year = {2013},
           title = {Central Limit Theorems for an Indian Buffet Model with Random Weights},
          author = {Patrizia Berti and Irene Crimaldi and Luca Pratelli and Pietro Rigo},
           month = {April},
            type = {Technical Report},
     institution = {IMT Institute for Advanced Studies Lucca},
            note = {Preprint, Submitted},
        keywords = {Bayesian nonparametrics, Central limit theorem, Conditional identity in distribution, Indian buffet process, Random measure, Random reinforcement, Stable convergence},
             url = {http://eprints.imtlucca.it/1544/},
        abstract = {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 
\{{$\backslash$}bar K\}\_n=(1/n){$\backslash$}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 \{{$\backslash$}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.}
}