## Bayesian Input Variable Selection Using Posterior Probabilities and Expected Utilities (2002)

Citations: | 6 - 1 self |

### BibTeX

@TECHREPORT{Vehtari02bayesianinput,

author = {Aki Vehtari and Jouko Lampinen},

title = {Bayesian Input Variable Selection Using Posterior Probabilities and Expected Utilities},

institution = {},

year = {2002}

}

### OpenURL

### Abstract

We consider the input variable selection in complex Bayesian hierarchical models. Our goal is to find a model with the smallest number of input variables having statistically or practically at least the same expected utility as the full model with all the available inputs. A good estimate for the expected utility can be computed using cross-validation predictive densities. In the case of input selection and a large number of input combinations, the computation of the cross-validation predictive densities for each model easily becomes computationally prohibitive. We propose to use the posterior probabilities obtained via variable dimension MCMC methods to find out potentially useful input combinations, for which the final model choice and assessment is done using the expected utilities.