Bayesian estimation is a statistical method for estimating the parameters of a model by updating prior beliefs with new evidence. Unlike traditional frequentist estimation methods, Bayesian estimation incorporates prior knowledge or beliefs about the parameters into the analysis. This approach allows for the modeling of uncertainty and variability in a more flexible and interpretable way. By incorporating prior beliefs and updating them based on new data, Bayesian estimation can provide more accurate and robust estimates of parameters. It is commonly used in a wide range of fields such as machine learning, econometrics, and biology.