Bayesian nonparametric inference for discovery probabilities: credible intervals and large sample asymptotics

Abstract

Given a sample of size $n$ from a population of individuals belonging to different species with unknown proportions, a popular problem of practical interest consists in making inference on the probability $D_n(l)$ that the $(n+1)$-th draw coincides with a species with frequency $l$ in the sample, for any $l=0,1,\dots ,n$. This paper contributes to the methodology of Bayesian nonparametric inference for $D_n(l)$. Specifically, under the general framework of Gibbs-type priors we show how to derive credible intervals for a Bayesian nonparametric estimation of $D_n(l)$, and we investigate the large $n$ asymptotic behaviour of such an estimator. Of particular interest are special cases of our results obtained under the specification of the two parameter Poisson--Dirichlet prior and the normalized generalized Gamma prior, which are two of the most commonly used Gibbs-type priors. With respect to these two prior specifications, the proposed results are illustrated through a simulation study and a benchmark Expressed Sequence Tags dataset. To the best our knowledge, this illustration provides the first comparative study between the two parameter Poisson--Dirichlet prior and the normalized generalized Gamma prior in the context of Bayesian nonparemetric inference for $D_n(l)$.

References in corpus (4)

• Are Gibbs-type priors the most natural generalization of the Dirichlet process?
• Poisson Process Partition Calculus with applications to Exchangeable models and Bayesian Nonparametrics
• Estimation of sums of random variables: Examples and information bounds
• Full Bayesian inference with hazard mixture models

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