BNN represents an advanced ML approach designed to estimate prediction uncertainty through Bayesian inference. Unlike traditional neural networks, whose training outcomes can vary significantly due to randomly initialized parameters and the stochastic nature of gradient descent, BNN introduces a probabilistic layer to this process. By treating the model's parameters as random variables with specified prior distributions, BNN uses the available labeled data to compute posterior distributions for each parameter. This is accomplished via Bayesian inference techniques such as Variational Inference (VI) or Markov Chain Monte Carlo (MCMC). Consequently, each prediction made by a BNN is drawn from a distribution, rather than being a single point estimate.