Bayesian model selection: The steepest mountain to climb
| Autor: | Iris E. Hendriks, Robert Bob O'Hara, Giacomo Tavecchia, Simone Tenan |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Variable selection
Computer science Bayesian probability Bayesian analysis Feature selection Model selection Bayesian inference Machine learning computer.software_genre symbols.namesake Econometrics Hierarchical modelling Selection (genetic algorithm) Statistical hypothesis testing business.industry Ecological Modeling Markov chain Monte Carlo Maximization BUGS language Hypothesis testing symbols Artificial intelligence business computer |
| Zdroj: | Digital.CSIC. Repositorio Institucional del CSIC instname Ecological modelling 283 (2014): 62–69. doi:10.1016/j.ecolmodel.2014.03.017 info:cnr-pdr/source/autori:Tenan, Simone; O'Hara, Robert B.; Hendriks, Iris; Tavecchia, Giacomo/titolo:Bayesian model selection: The steepest mountain to climb/doi:10.1016%2Fj.ecolmodel.2014.03.017/rivista:Ecological modelling/anno:2014/pagina_da:62/pagina_a:69/intervallo_pagine:62–69/volume:283 |
| ISSN: | 0304-3800 |
| DOI: | 10.1016/j.ecolmodel.2014.03.017 |
| Popis: | Following the advent of MCMC engines Bayesian hierarchical models are becoming increasingly common for modelling ecological data. However, the great enthusiasm for model fitting has not yet encompassed the selection of competing models, despite its fundamental role in the inferential process. This contribution is intended as a starting guide for practical implementation of Bayesian model and variable selection into a general purpose software in BUGS language. We explain two well-known procedures, the product space method and the Gibbs variable selection, clarifying theoretical aspects and practical guidelines through applied examples on the comparison of non-nested models and on the selection of variables in a generalized linear model problem. Despite the relatively wide range of available techniques and the difficulties related to the maximization of sampling efficiency, for their conceptual simplicity and ease of implementation the proposed methods represent useful tools for ecologists and conservation biologists that want to close the loop of a Bayesian analysis. © 2014 Elsevier B.V. Funds were partially provided by the Regional Government of Balearic Islands and FEDER funding. ST was funded by a PhD grant from the Muse – Museo delle Scienze (Trento, Italy) in collaboration with the University of Pavia |
| Databáze: | OpenAIRE |
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