Recent approaches to development accounting reflect substantial model uncertainty at both the instrument and the development determinant level. Bayesian Model Averaging (BMA) has been proven useful in resolving model uncertainty in economics, and we extend BMA to formally account for model uncertainty in the presence of endogeneity. The new methodology is shown to be highly efficient and to red...

Datasets of population dynamics are typically characterized by a short temporal extension. In this condition, several alternative models typically achieve close accuracy, though returning quite different predictions (model uncertainty ). Bayesian model averaging (BMA) addresses this issue by averaging the prediction of the different models, using as weights the posterior probability of the mode...

We compare Bayes Model Averaging, BMA, to a non-Bayes form of model averaging called stacking. In stacking, the weights are no longer posterior probabilities of models; they are obtained by a technique based on cross-validation. When the correct data generating model (DGM) is on the list of models under consideration BMA is never worse than stacking and often is demonstrably better, provided th...

A prognosis model has been developed for solid waste generation from households in Hoi An City, a famous tourist city in Viet Nam. Waste sampling, followed by a questionnaire survey, was carried out to gather data. The Bayesian model average method was used to identify factors significantly associated with waste generation. Multivariate linear regression analysis was then applied to evaluate th...

A probabilistic precipitation forecasting model using generalized additive models (GAMs) and Bayesian model averaging (BMA) was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedi...

Forecast ensembles typically show a spread-skill relationship, but they are also often underdispersive, and therefore uncalibrated. Bayesian model averaging (BMA) is a statistical postprocessing method for forecast ensembles that generates calibrated probabilistic forecast products for weather quantities at individual sites. This paper introduces the Spatial BMA technique, which combines BMA an...

Naive Bayes (NB) is well-known to be a simple but effective classifier, especially when combined with feature selection. Unfortunately, feature selection methods are often greedy and thus cannot guarantee an optimal feature set is selected. An alternative to feature selection is to use Bayesian model averaging (BMA), which computes a weighted average over multiple predictors; when the different...

Model-based estimation of the effect of an exposure on an outcome is generally sensitive to the choice of which confounding factors are included in the model. We propose a new approach, which we call Bayesian adjustment for confounding (BAC), to estimate the effect of an exposure of interest on the outcome, while accounting for the uncertainty in the choice of confounders. Our approach is based...

We propose a method for post-processing an ensemble of multivariate forecasts in order to obtain a joint predictive distribution of weather. Our method utilizes existing univariate postprocessing techniques, in this case ensemble Bayesian model averaging (BMA), to obtain estimated marginal distributions. However, implementing these methods individually offers no information regarding the joint ...

Estimating the risks heat waves pose to human health is a critical part of assessing the future impact of climate change. In this article, we propose a flexible class of time series models to estimate the relative risk of mortality associated with heat waves and conduct Bayesian model averaging (BMA) to account for the multiplicity of potential models. Applying these methods to data from 105 U....