Bayesian model selection: The steepest mountain to climb
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DOI: 10.1016/j.ecolmodel.2014.03.017
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References listed on IDEAS
- Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
- 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.
- 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.
- Ntzoufras, Ioannis, 2002. "Gibbs Variable Selection using BUGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i07).
- 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.
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- 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.
- 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).
- Laplanche, Christophe & Leunda, Pedro M. & Boithias, Laurie & Ardaíz, José & Juanes, Francis, 2019. "Advantages and insights from a hierarchical Bayesian growth and dynamics model based on salmonid electrofishing removal data," Ecological Modelling, Elsevier, vol. 392(C), pages 8-21.
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Keywords
Bayesian analysis; BUGS language; Hierarchical modelling; Hypothesis testing; Model selection; Variable selection;All these keywords.
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