Bayesian estimation is a statistical method that combines prior knowledge with new evidence to make inferences about unknown parameters. It uses Bayes' theorem, which updates the probability of a hypothesis as more data becomes available. This approach allows for a more flexible analysis, as it incorporates both existing beliefs and observed data. In Bayesian estimation, the prior distribution represents what is known before observing the data, while the likelihood function describes how likely the observed data is given the parameters. The result is a posterior distribution that reflects updated beliefs, providing a comprehensive view of uncertainty in the estimation process.