Bayesian Model Averaging (BMA) is a statistical technique that incorporates uncertainty about which model is the best for predicting outcomes by averaging over a set of candidate models, weighted by their posterior probabilities. This method not only helps to improve predictions by considering multiple models, but it also mitigates the risk of relying too heavily on any single model, thus providing a more robust framework for decision-making.
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