Bayesian Neural Networks (BNNs) are probabilistic models that capture uncertainty in predictions by modeling both aleatoric (data noise) and epistemic (model uncertainty). They incorporate Bayesian principles into neural networks, enabling quantification of uncertainty and improved decision-making. BNNs leverage techniques like Markov Chain Monte Carlo (MCMC) for posterior inference and provide valuable insights into the reliability and robustness of model predictions.
