A Bayesian neural network is a neural network that represents uncertainty in its weights, predictions, or outputs using Bayesian methods. Instead of producing only a single estimate, it can model a distribution over possible outcomes. This makes it useful in domains where uncertainty quantification is important, such as medicine, safety, and risk-sensitive decision support.
A probabilistic neural network that accounts for uncertainty in weights and outputs. A standard neural network regression model typically predicts a scalar value; for example, a standard model predicts a house price of 853,000. In contrast, a Bayesian neural network predicts a distribution of values; for example, a Bayesian model predicts a house price of 853,000 with a standard deviation of 67,200. A Bayesian neural network relies on Bayes' Theorem to calculate uncertainties in weights and predictions. A Bayesian neural network can be useful when it is important to quantify uncertainty, such as in models related to pharmaceuticals. Bayesian neural networks can also help prevent overfitting.