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Please use this identifier to cite or link to this item: http://hdl.handle.net/10805/1411

Title: Bayesian Modeling of Presence-only Data
Authors: GOLINI, NATALIA
Tutor: Jona Lasinio, Giovanna
Divino, Fabio
Keywords: Bayesian models, Data augmentation, MCMC algorithm, Presence-only data, Potential distribution, Pseudo-absence approach, Semicontinuous data, Spatial statistics, Two-part model.
Issue Date: 15-Mar-2012
Abstract: This thesis develops models and methods for statistical analysis of presence-only data. Besides constructing new models, the emphasis is on the theoretical characteristics of new models and on Bayesian prediction. Monte Carlo Markov chains algorithms are developed for the new presence-only data models in order to be able to simulate the posterior distribution of the unknowns and the predictive distribution of variable of interest. The new methods are applied to simulated data. One application in ecologic science have been a driving force behind the work.
URI: http://hdl.handle.net/10805/1411
Appears in PhD:STATISTICA METODOLOGICA

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