Bayesian Ensemble

Bayesian ensembles combine multiple models, often neural networks, to improve prediction accuracy and robustness by leveraging the diversity of their predictions and quantifying uncertainty. Current research focuses on efficient ensemble methods, including those based on Bayesian neural networks, stochastic neural networks, and sparse subnetworks, with a particular emphasis on optimizing weighting schemes and addressing computational costs. This approach is proving valuable in diverse applications, from battery prognostics and environmental monitoring to medical image analysis and financial forecasting, by providing more reliable and interpretable predictions than single models.