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Bayesian model selection: The steepest mountain to climb

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  • Tenan, Simone
  • O’Hara, Robert B.
  • Hendriks, Iris
  • Tavecchia, Giacomo

Abstract

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.

Suggested Citation

  • Tenan, Simone & O’Hara, Robert B. & Hendriks, Iris & Tavecchia, Giacomo, 2014. "Bayesian model selection: The steepest mountain to climb," Ecological Modelling, Elsevier, vol. 283(C), pages 62-69.
    • Handle: RePEc:eee:ecomod:v:283:y:2014:i:c:p:62-69
    DOI: 10.1016/j.ecolmodel.2014.03.017
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    References listed on IDEAS

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    1. Russell B. Millar, 2009. "Comparison of Hierarchical Bayesian Models for Overdispersed Count Data using DIC and Bayes' Factors," Biometrics, The International Biometric Society, vol. 65(3), pages 962-969, September.
    2. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    3. Ntzoufras, Ioannis, 2002. "Gibbs Variable Selection using BUGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i07).
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    5. Sisson, Scott A., 2005. "Transdimensional Markov Chains: A Decade of Progress and Future Perspectives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1077-1089, September.
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    Cited by:

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    2. Simone Tenan & Paolo Pedrini & Natalia Bragalanti & Claudio Groff & Chris Sutherland, 2017. "Data integration for inference about spatial processes: A model-based approach to test and account for data inconsistency," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-18, October.
    3. Tenan, S. & Maffioletti, C. & Caccianiga, M. & Compostella, C. & Seppi, R. & Gobbi, M., 2016. "Hierarchical models for describing space-for-time variations in insect population size and sex-ratio along a primary succession," Ecological Modelling, Elsevier, vol. 329(C), pages 18-28.
    4. Palamara, Gian Marco & Dennis, Stuart R. & Haenggi, Corinne & Schuwirth, Nele & Reichert, Peter, 2022. "Investigating the effect of pesticides on Daphnia population dynamics by inferring structure and parameters of a stochastic model," Ecological Modelling, Elsevier, vol. 472(C).

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