Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2017

Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices

  • Identification data

    Identifier: RP:2464
    Handle: https://hdl.handle.net/20.500.11797/RP2464
    Authors:
    Barber, AntoniMorales, JavierMayoral, AsuncionLopez-Quílez, AntonioConesa, DavidBarber, Xavier
    Abstract:
    A methodological approach for modelling the spatial distribution of bioclimatic indices is proposed in this paper. The value of the bioclimatic index is modelled with a hierarchical Bayesian model that incorporates both structured and unstructured random effects. Selection of prior distributions is also discussed in order to better incorporate any possible prior knowledge about the parameters that could refer to the particular characteristics of bioclimatic indices. MCMC methods and distributed programming are used to obtain an approximation of the posterior distribution of the parameters and also the posterior predictive distribution of the indices. One main outcome of the proposal is the spatial bioclimatic probability distribution of each bioclimatic index, which allows researchers to obtain the probability of each location belonging to different bioclimates. The methodology is evaluated on two indices in the Island of Cyprus.

  • Others:

    URV's Author/s: Barber, Antoni Morales, Javier Mayoral, Asuncion Lopez-Quílez, Antonio Conesa, David Barber, Xavier
    Keywords: Bioclimatology, geostatistics, parallel computation, spatial prediction
    Abstract: A methodological approach for modelling the spatial distribution of bioclimatic indices is proposed in this paper. The value of the bioclimatic index is modelled with a hierarchical Bayesian model that incorporates both structured and unstructured random effects. Selection of prior distributions is also discussed in order to better incorporate any possible prior knowledge about the parameters that could refer to the particular characteristics of bioclimatic indices. MCMC methods and distributed programming are used to obtain an approximation of the posterior distribution of the parameters and also the posterior predictive distribution of the indices. One main outcome of the proposal is the spatial bioclimatic probability distribution of each bioclimatic index, which allows researchers to obtain the probability of each location belonging to different bioclimates. The methodology is evaluated on two indices in the Island of Cyprus.
    Journal publication year: 2017
    Publication Type: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article

  • Keywords:

    Bioclimatology, geostatistics, parallel computation, spatial prediction

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