gvs_BUGS

Gibbs Variable Selection using BUGS. In this paper we discuss and present in detail the implementation of Gibbs variable selection as defined by Dellaportas et al. (2000, 2002) using the BUGS software (Spiegelhalter et al. , 1996a,b,c). The specification of the likelihood, prior and pseudo-prior distributions of the parameters as well as the prior term and model probabilities are described in detail. Guidance is also provided for the calculation of the posterior probabilities within BUGS environment when the number of models is limited. We illustrate the application of this methodology in a variety of problems including linear regression, log-linear and binomial response models.

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References in zbMATH (referenced in 4 articles , 1 standard article )

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  1. Heck, Daniel W.; Overstall, Antony M.; Gronau, Quentin F.; Wagenmakers, Eric-Jan: Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models (2019)
  2. Whitney, Melissa; Ryan, Louise; Walkowiak, Jens: On the use of Bayesian model averaging for covariate selection in epidemiological modeling (2013)
  3. Ozkok, Erengul; Streftaris, George; Waters, Howard R.; Wilkie, A. David: Bayesian modelling of the time delay between diagnosis and settlement for critical illness insurance using a Burr generalised-linear-type model (2012)
  4. Ioannis Ntzoufras: Gibbs Variable Selection using BUGS (2002) not zbMATH