Bayesian Estimation
Bayesian estimation is a statistical inference method that updates the probability for a hypothesis as more evidence or information becomes available. It uses Bayes' theorem to combine prior knowledge (prior distribution) with observed data (likelihood) to form a posterior distribution, which represents updated beliefs about parameters. This approach is fundamental in probabilistic modeling, machine learning, and decision-making under uncertainty.
Developers should learn Bayesian estimation when working on projects involving uncertainty quantification, such as A/B testing, recommendation systems, or predictive modeling in data science and machine learning. It is particularly useful in scenarios where prior information is available (e.g., from historical data) and needs to be integrated with new observations, allowing for more robust and interpretable inferences compared to frequentist methods.