Antonio Lijoi (Pavia) Gibbs type priors for Bayesian nonparametric inference on species variety 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.