Bayesian Nonparametric Inference for "Species-sampling" Problems

TL;DR

This paper reviews Bayesian nonparametric methods for species-sampling problems, focusing on the Pitman--Yor process, offering improved computational approaches, analyzing parameter estimation consistency, and discussing generalizations in biological and related sciences.

Contribution

It introduces novel posterior representations for SSPs under the Pitman--Yor process, and analyzes the Bayesian consistency of parameter estimation methods.

Findings

Posterior representations simplified using Binomial and Hypergeometric distributions.

Bayesian consistency established for the discount parameter estimation.

Scale parameter cannot be estimated consistently, advising caution.

Abstract

Given an observed sample from a population of individuals belonging to species, "species-sampling" problems (SSPs) call for estimating some features of the unknown species composition of additional unobservable samples from the same population. Within SSPs, the problems of estimating coverage probabilities, the number of unseen species and coverages of prevalences have emerged in the past three decades for being the subject of numerous methodological and applied works, mostly in biological sciences but also in statistical machine learning, electrical engineering, theoretical computer science, information theory and forensic statistics. In this paper, we focus on these popular SSPs, and present an overview of their Bayesian nonparametric (BNP) analysis under the Pitman--Yor process (PYP) prior. While reviewing the literature, we improve on computation and interpretability of existing posterior inferences, typically expressed through complicated combinatorial numbers, by establishing novel posterior representations in terms of simple compound Binomial and Hypergeometric distributions. We also consider the problem of estimating the discount and scale parameters of the PYP prior, showing a property of Bayesian consistency with respect to estimation through the hierarchical Bayes and empirical Bayes approaches, that is: the discount parameter can be estimated consistently, whereas the scale parameter cannot be estimated consistently, thus advising caution in posterior inference. We conclude our work by discussing some generalizations of SSPs, mostly in the field of biological sciences, which deal with "feature-sampling", multiple populations of individuals sharing species and classes of Markov chains.