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Bayesian estimate


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.

Suppose an unknown parameter is known to have a prior distribution . Let be an estimator of (based on some measurements x), and let be a loss function, such as squared error. The Bayes risk of is defined as , where the expectation is taken over the probability distribution of : this defines the risk function as a function of . An estimator is said to be a Bayes estimator if it minimizes the Bayes risk among all estimators. Equivalently, the estimator which minimizes the posterior expected loss for each also minimizes the Bayes risk and therefore is a Bayes estimator.


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