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: | Objeto de conferencia |
| 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 |
| _version_ | 1855491985314414592 |
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| author | Vicent, Antonio Martínez-Minaya, Joaquín López-Quílez, Antonio Conesa, David |
| author_browse | Conesa, David López-Quílez, Antonio Martínez-Minaya, Joaquín Vicent, Antonio |
| author_facet | Vicent, Antonio Martínez-Minaya, Joaquín López-Quílez, Antonio Conesa, David |
| author_sort | Vicent, Antonio |
| collection | ReDivia |
| description | 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. |
| format | Objeto de conferencia |
| id | ReDivia5794 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| record_format | dspace |
| spelling | ReDivia57942025-04-25T14:51:16Z Bayesian hierarchical modelling of the olive quick decline syndrome in south-eastern Italy Vicent, Antonio Martínez-Minaya, Joaquín López-Quílez, Antonio Conesa, David 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. 2018-05-05T17:21:21Z 2018-05-05T17:21:21Z 2017 conferenceObject 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. http://hdl.handle.net/20.500.11939/5794 http://vabar.es/assets/vibass17/abstracts_book.pdf#page=27 en 1th VIBASS Workshop. Valencia International Bayesian Analysis Summer School Valencia, Spain Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ electronico |
| spellingShingle | Vicent, Antonio Martínez-Minaya, Joaquín López-Quílez, Antonio Conesa, David Bayesian hierarchical modelling of the olive quick decline syndrome in south-eastern Italy |
| title | Bayesian hierarchical modelling of the olive quick decline syndrome in south-eastern Italy |
| title_full | Bayesian hierarchical modelling of the olive quick decline syndrome in south-eastern Italy |
| title_fullStr | Bayesian hierarchical modelling of the olive quick decline syndrome in south-eastern Italy |
| title_full_unstemmed | Bayesian hierarchical modelling of the olive quick decline syndrome in south-eastern Italy |
| title_short | Bayesian hierarchical modelling of the olive quick decline syndrome in south-eastern Italy |
| title_sort | bayesian hierarchical modelling of the olive quick decline syndrome in south eastern italy |
| url | http://hdl.handle.net/20.500.11939/5794 http://vabar.es/assets/vibass17/abstracts_book.pdf#page=27 |
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