6533b85afe1ef96bd12b98d0
RESEARCH PRODUCT
Data Augmentation Approach in Bayesian Modelling of Presence-only Data
Fabio DivinoAntti PenttinenG. Jona LasinioNatalia Golinisubject
Data augmentationPresence-only dataComputer scienceBayesian probabilityLogistic regressionBayesian inferencePseudo-absence approachBayesian statisticsBayesian model; Data augmentation; MCMC algorithm; Potential distribution; Presence-only data; Pseudo-absence approachBayesian model Data augmentation MCMC algorithm Presence-only data Pseudo-absence approach Potential distributionpotentialdistributionBayesian modelBayesian multivariate linear regressionPotential distributionStatisticsCovariateEconometricsGeneral Earth and Planetary Sciencespseudo-absence approach; potentialdistribution.; data augmentation; presence-only data; potential distribution; mcmc algorithm; bayesian modelBayesian linear regressionBayesian averageMCMC algorithmGeneral Environmental Sciencedescription
Abstract Ecologists are interested in prediction of potential distribution of species in suitable areas, essential for planning conservation and management strategies. Unfortunately, often the only available information in such studies is the true presence of the species at few locations of the study area and the associated environmental covariates over the entire area, referred as presence-only data. We propose a Bayesian approach to estimate logistic linear regressions adapted to presence-only data through the introduction of a random approximation of the correction factor in the adjusted logistic model that allows us to overcome the need to know a priori the prevalence of the species.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2011-01-01 | Procedia Environmental Sciences |