How do Bayesian methods improve uncertainty estimation in machine learning models?

Bayesian methods provide a principled framework for estimating uncertainty in machine learning models by representing uncertainty as probability distributions over model parameters and predictions. Bayesian models incorporate prior knowledge and update their beliefs based on observed data, allowing for more robust and reliable uncertainty quantification compared to traditional point estimates.