Bayesian learning is a statistical approach based on Bayes' theorem, which allows for the updating of beliefs or hypotheses based on new evidence or data. In this approach, prior knowledge is combined with new data to form a posterior probability distribution that represents updated beliefs. Bayesian learning is used in various fields such as machine learning, artificial intelligence, and cognitive science to make predictions, infer causal relationships, and make decisions based on uncertain information. It is considered a powerful and versatile method for modeling complex systems and reasoning under uncertainty.