Given a sample of size n from a population of species with unknown pro- portions, a common problem of practical interest consists in making inference on the probability Dn(l) that the (n+1)-th draw coincides with a species with frequency l in the sample, for any l ≥ 0. Under the general framework of Gibbs-type priors we show how to derive credible intervals for a Bayesian nonparametric estimator of Dn(l).
On Bayesian nonparametric inference for discovery probabilities
FAVARO, STEFANO;
2016-01-01
Abstract
Given a sample of size n from a population of species with unknown pro- portions, a common problem of practical interest consists in making inference on the probability Dn(l) that the (n+1)-th draw coincides with a species with frequency l in the sample, for any l ≥ 0. Under the general framework of Gibbs-type priors we show how to derive credible intervals for a Bayesian nonparametric estimator of Dn(l).
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