A0442
Title: Predictive inference for ecological problems
Authors: Federico Camerlenghi - University of Milano-Bicocca (Italy) [presenting]
Lorenzo Ghilotti - University of Milano-Bicocca (Italy)
Tommaso Rigon - University of Milano-Bicocca (Italy)
Abstract: It is emphasized that a fundamental goal of science is prediction rather than the explanation of observed facts. This idea has also been pointed out by another statistician, who wrote, "Science cannot limit itself to theorizing about accomplished facts but must foresee". The Bayesian nonparametric approach offers a natural probabilistic framework to address this fundamental issue through the notion of predictive distributions. The purpose is to consider a population of animals composed of different species with unknown proportions and to address prediction problems in this context. An archetypal problem in the species setting is the estimation of the unseen: Given an initial, observable sample from a population, how many new species will be observed in a future sample from the same population? While Bayesian nonparametric methods traditionally concentrate on abundance data, the scenario of incidence data is considered, where the sampling unit is a plot, and one records the incidence (presence or absence) of a species in the plot. A new Bayesian nonparametric approach is developed, designed for incidence data, providing closed-form expression to address several prediction problems, including the estimation of unseen species and population size. The importance of the findings is showcased in facing biodiversity estimation in a large variety of ecological frameworks.
