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  "body": "\n## Summary\n\nAn empirical Bayes method estimates the [[prior distribution|00NBBSXRQS]] from the data and then uses it as if it were known.\nIt applies when many similar parameters are estimated at once, so that the data on all of them show how the parameters are spread.\nIt sits between full [[Bayesian inference|0JWRJX2RMP]] and frequentist estimation, and it explains shrinkage estimators such as the [[James–Stein estimator|709T196XCT]].\n\n## Explanations\n\n### L1 human\n\nSuppose you rate a thousand baseball players, each from a short season.\nEach player's own average is noisy, but the thousand averages together show how batting skill is spread in the league.\nEmpirical Bayes uses that spread as a prior and pulls each player's average a bit towards the league average.\nThe pull is strong for players with little data and weak for players with a lot of data.\n",
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