In statistics and signal processing, the orthogonality principle is a necessary and sufficient condition for the optimality of a Bayesian estimator. Loosely stated, the orthogonality principle says that the error vector of the optimal estimator (in a mean square error sense) is orthogonal to any possible estimator. The orthogonality principle is most commonly stated for linear estimators, but more general formulations are possible. Since the principle is a necessary and sufficient condition for optimality, it can be used to find the minimum mean square error estimator.
Read more about Orthogonality Principle: Orthogonality Principle For Linear Estimators, General Formulation, A Solution To Error Minimization Problems, See Also
Famous quotes containing the word principle:
“It is funny that men who are supposed to be scientific cannot get themselves to realise the basic principle of physics, that action and reaction are equal and opposite, that when you persecute people you always rouse them to be strong and stronger.”
—Gertrude Stein (1874–1946)