BVM (Bayesian Variational Methods)

Bayesian Variational Methods (BVM) are techniques used in machine learning to estimate uncertainty in models' predictions. They work by approximating complex probability distributions that represent what a model "believes" about the data. Instead of calculating exact probabilities (which is often impossible), BVM finds simpler, manageable distributions that closely match the true ones. This allows the model to not only make predictions but also to understand how confident it is about those predictions. In essence, BVM provides a way for models to learn and reason about uncertainty in a computationally efficient manner.