This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 597
Type: Invited
Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #305929
Title: Gibbs-Type Priors for Bayesian Nonparametric Inference on Species Variety
Author(s): Stefano Favaro and Antonio Lijoi*+ and Ramses Mena and Igor Pruenster
Companies: University of Turin and University of Pavia and IIMAS-UNAM and University of Turin
Address: Department of Economics and Quantitative Methods, Pavia, International, 27100, Italy
Keywords: Bayesian nonparametrics ; Gibbs-type prior ; Predictive distribution ; Species sampling
Abstract:

Sampling problems from populations which are made of different species arise in a variety of ecological and biological contexts. Basing on a sample of size n, one of the main statistical goals is the evaluation of species richness. For example, in the analysis of Expressed Sequence Tags (EST) data, which are generated by sequencing cDNA libraries consisting of millions of genes, one is interested in predicting the number of new gene species that will be observed in an additional sample of size m or in estimating the so-called sample coverage. In order to deal with these issues, we undertake a Bayesian nonparametric approach based on Gibbs-type priors which include, as special cases, the Dirichlet and the two-parameter Poisson-Dirichlet processes. We show how a full Bayesian analysis can be performed and describe the corresponding computational algorithm.


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