Bayes estimator
| Part of a series on |
| Bayesian statistics |
|---|
| Posterior = Likelihood × Prior ÷ Evidence |
| Background |
| Model building |
| Posterior approximation |
| Estimators |
| Evidence approximation |
| Model evaluation |
In estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value of a loss function (i.e., the posterior expected loss). Equivalently, it maximizes the posterior expectation of a utility function. An alternative way of formulating an estimator within Bayesian statistics is maximum a posteriori estimation.