Bayesian model averaging (BMA) methods are regularly used to deal with model uncertainty in regression models. This paper shows how to introduce Bayesian model averaging methods in quantile regressions, and allow for di¤erent predictors to a¤ect di¤erent quantiles of the dependent variable. I show that quantile regression BMA methods can help reduce uncertainty regarding outcomes of future in‡a...

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 ...

In this paper, we suggest an empirical Bayes-type prior for the model space in Bayesian model averaging (BMA) in a method we call tuned Bayesian model averaging (tBMA). This method relies on leave-one-out cross validation to choose a hyper-parameter that will cause the averaging process to favor either smaller or richer models in the prior distribution over the models. We find that this method ...

The methodology of Bayesian Model Averaging (BMA) is applied for assessment of newborn brain maturity from sleep EEG. In theory this methodology provides the most accurate assessments of uncertainty in decisions. However, the existing BMA techniques have been shown providing biased assessments in the absence of some prior information enabling to explore model parameter space in details within a...

Abstract. Bayesian model averaging is flawed in the M-open setting in which the true data-generating process is not one of the candidate models being fit. We take the idea of stacking from the point estimation literature and generalize to the combination of predictive distributions. We extend the utility function to any proper scoring rule and use Pareto smoothed importance sampling to efficien...

Several variants of Bayesian Model Averaging (BMA) are described and evaluated on a model library of heterogeneous classifiers, and compared to other classifier combination methods. In particular, embedded cross-validation is investigated as a technique for reducing overfitting in BMA.

Model uncertainty remains a challenge to researchers in different applications. When many competing models are available for estimation, and without enough guidance from theory, model averaging represents an alternative to model selection. Despite model averaging approaches have been present in statistics for many years, only recently they are starting to receive attention in applications. The ...

We deal with the arbitrariness in the choice of the prior over the models in Bayesian model averaging (BMA), by modelling prior knowledge by a set of priors (i.e., a prior credal set). We consider Dash and Cooper’s BMA applied to naive Bayesian networks, replacing the single prior over the naive models by a credal set; this models a condition close to prior ignorance about the models, which lea...

Abstract The widely recommended procedure of Bayesian model averaging is flawed in the M-open setting in which the true data-generating process is not one of the candidate models being fit. We take the idea of stacking from the point estimation literature and generalize to the combination of predictive distributions, extending the utility function to any proper scoring rule, using Pareto smooth...

This document provides the statistical background for the Bayesian model averaging continual reassessment method (BMA-CRM). The BMA-CRM is a Bayesian model-based phase I clinical trial design. The primary goal of the BMA-CRM is to identify the maximum tolerated dose (MTD) of a new drug, which is typically defined as the dose with a doselimiting toxicity (DLT) probability that is closest to the ...