problem. Of equal importance though, the Bayes Rule reflects consideration of loss outcomes under different states of nature, .

Examples in statistics

* For a scalar parameter , a decision function whose output is an estimate of , and a quadratic loss function (squared error loss) the risk function becomes the mean squared error of the estimate, An estimator found by minimizing the mean squared error