Empirical Bayes Estimation

Empirical Bayes estimation is a statistical technique that combines observed data with prior knowledge to improve estimates of unknown parameters. It starts by analyzing multiple datasets to learn about the overall distribution of the parameters (the “prior”), then uses this information to refine estimates for individual cases. This approach effectively balances individual data with the broader trend observed across many cases, leading to more accurate and stable estimates, especially when data are limited or noisy. It’s widely used in fields like healthcare, marketing, and genetics to make smarter predictions by leveraging both data and learned patterns.