Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Concept
Bayes estimator
Summary
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.
If the prior is improper then an estimator which minimizes the posterior expected loss for each is called a generalized Bayes estimator.
Minimum mean square error
The most common risk function used for Bayesian estimation is the mean square error (MSE), also called squared error risk. The MSE is defined by
where the expectation is taken over the joint distribution of and .
Using the MSE as risk, the Bayes estimate of the unknown parameter is simply the mean of the posterior distribution,
This is known as the minimum mean square error (MMSE) estimator.
Conjugate prior
If there is no inherent reason to prefer one prior probability distribution over another, a conjugate prior is sometimes chosen for simplicity. A conjugate prior is defined as a prior distribution belonging to some parametric family, for which the resulting posterior distribution also belongs to the same family. This is an important property, since the Bayes estimator, as well as its statistical properties (variance, confidence interval, etc.), can all be derived from the posterior distribution.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood