Bayesian hierarchical modelling of the olive quick decline syndrome in south-eastern Italy
In the last years, the use of complex statistical models has increased to improve our knowledge on the spread of diseases and the distribution of species, being of great interest in plant disease epidemiology. The complexity of these models makes the inferential and predictive processes challenging...
| Main Authors: | , , , |
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| Format: | conferenceObject |
| Language: | Inglés |
| Published: |
2018
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| Online Access: | http://hdl.handle.net/20.500.11939/5794 http://vabar.es/assets/vibass17/abstracts_book.pdf#page=27 |
| Summary: | In the last years, the use of complex statistical models has increased to improve our knowledge on the spread of diseases and the distribution of species, being of great interest in plant
disease epidemiology. The complexity of these models makes the inferential and predictive processes challenging to perform. Bayesian statistics represents a good alternative, because it is
based on the premise that both information and uncertainty can be expressed in terms of probability distributions. Despite the advantages of Bayesian inference, the main challenge is to find
an analytic expression for posterior distributions of the parameters and hyperparameters. Several
numeric approaches have been proposed, such as Markov chain Monte Carlo methods (MCMC)
and integrated nested Laplace approximation (INLA). Here, we present different spatio-temporal
analyses using INLA for the geographical spread of the olive quick decline syndrome, a lethal
plant disease caused by the bacterium Xylella fastidiosa in south-eastern Italy. |
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