Credal model averaging of logistic regression for modeling the distribution of marmot burrows

Bayesian model averaging (BMA) weights the inferences produced by a set of competing models, using as weights the models posterior probabilities. An open problem of BMA is how to set the prior probability of the models. Credal model averaging (CMA) is a credal ensemble of Bayesian models, which generalizes BMA by substituting the single prior over the models by a set of priors. The base models of the ensemble are learned in a Bayesian fashion. We use CMA to ensemble base classi ers which are Bayesian logistic regressors, characterized by di erent sets of covariates. CMA returns indeterminate classi cations when the classi cation is prior-dependent, namely when the most probable class depends on the prior probability assigned to the di erent models. We apply CMA for modelling the presence and absence of marmot burrows in an Alpine valley in Italy and show that it compares favorably to BMA.