RT conferenceObject
T1 Bayesian hierarchical modelling of the olive quick decline syndrome in south-eastern Italy
A1 Vicent, Antonio
A1 Martínez-Minaya, Joaquín
A1 López-Quílez, Antonio
A1 Conesa, David
AB 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 plantdisease epidemiology. The complexity of these models makes the inferential and predictive processes challenging to perform. Bayesian statistics represents a good alternative, because it isbased 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 findan analytic expression for posterior distributions of the parameters and hyperparameters. Severalnumeric approaches have been proposed, such as Markov chain Monte Carlo methods (MCMC)and integrated nested Laplace approximation (INLA). Here, we present different spatio-temporalanalyses using INLA for the geographical spread of the olive quick decline syndrome, a lethalplant disease caused by the bacterium Xylella fastidiosa in south-eastern Italy.
YR 2017
FD 2017
LK http://hdl.handle.net/20.500.11939/5794
UL http://hdl.handle.net/20.500.11939/5794
LA en
NO Vicent, A., Martínez-Minaya, J., López-Quílez, A., Conesa, D. (2017). Bayesian hierarchical modelling of the olive quick decline syndrome in south-eastern Italy. In 1th VIBASS Workshop. Valencia International Bayesian Analysis Summer School, Valencia, Spain.
DS MINDS@UW
RD Dec 7, 2023