BMA

BMA, or Bayesian Model Averaging, is a statistical approach that considers multiple possible models to better understand data. Instead of relying on a single best model, BMA combines predictions from all plausible models, weighing each by how well it explains the data. This method accounts for model uncertainty, leading to more reliable and robust estimates. Essentially, BMA acknowledges that we rarely know the perfect model and averages over many, improving decision-making and inference in complex situations.