Beyond Point Predictions: A Practical Introduction to Bayesian Neural Networks

Standard machine learning models provide single-point predictions, like a specific house price, but fail to communicate how confident they are in that estimate. Bayesian Neural Networks (BNNs) solve this by replacing each fixed weight within the network with a full probability distribution. By sampling from these distributions for each prediction, a BNN can generate a range of plausible outcomes, revealing when a prediction should or shouldn't be trusted. Since calculating these distributions exactly is computationally impossible, a technique called Variational Inference is used to approximate them by turning the complex calculation into a more manageable optimization problem.