Bayesian Neural Networks (BNNs) are a type of artificial neural network that incorporate principles from Bayesian statistics. Unlike traditional neural networks, which provide point estimates for weights, BNNs treat weights as probability distributions. This allows them to quantify uncertainty in their predictions, making them more robust in situations with limited data or noisy inputs. In BNNs, the learning process involves updating these weight distributions based on observed data. This is achieved through techniques like Markov Chain Monte Carlo (MCMC) or Variational Inference. As a result, BNNs can provide not only predictions but also confidence intervals, enhancing decision-making in various applications.