Improved Bayesian multimodeling: Integration of copulas and Bayesian model averaging

Improved Bayesian Multi-Modeling: Integration of Copulas and Bayesian Model Averaging

Predicting waste generation using Bayesian model averaging

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...

Multivariate probabilistic forecasting using Bayesian model averaging and copulas

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 ...

Tuned Bayesian Model Averaging

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 ...

Bayesian Model Averaging

Bayesian Model averaging is a natural response to model uncertainty. It has become an important practical tool for dealing with model uncertainty, in particular in empirical settings with large numbers of potential models and relatively limited numbers of observations. Most of this paper focuses on the problem of variable selection in normal linear regression models, but also briefly considers ...